File size: 2,259 Bytes
acf77ab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | import json
from pathlib import Path
def adapt_mbpp_task(entry: dict) -> dict:
"""Convert MBPP entry to CodeForge training format."""
task_id = entry["task_id"]
brief = entry["text"]
# Starter file: empty main.py
initial_files = {"main.py": "# Write your solution here\n"}
# Hidden tests: wrap MBPP asserts into pytest functions
test_lines = []
setup = entry.get("test_setup_code", "")
if setup:
test_lines.append(setup)
test_lines.append("from main import *\n")
for i, assert_str in enumerate(entry.get("test_list", [])):
test_lines.append(f"def test_{i}():\n {assert_str}\n")
hidden_tests = {"test_hidden.py": "\n".join(test_lines)}
return {
"task_id": str(task_id),
"brief": brief,
"initial_files": initial_files,
"hidden_tests": hidden_tests,
"tools": ("ruff", "imports", "mypy", "pytest"),
}
def load_mbpp_dataset(dataset_path: str = "dataset/mbpp.jsonl", split: str = "train"):
"""Load MBPP tasks and map them to CodeForge format suitable for TRL."""
import datasets
tasks = []
path = Path(dataset_path)
if not path.exists():
raise FileNotFoundError(f"MBPP dataset not found at {path}")
with open(path, "r", encoding="utf-8") as f:
for line in f:
if not line.strip(): continue
tasks.append(json.loads(line))
# Split: 1-874 Train, 875-974 Eval
if split == "train":
tasks = tasks[:874]
else:
tasks = tasks[874:]
formatted_tasks = [adapt_mbpp_task(t) for t in tasks]
# Create huggingface dataset
def gen():
for t in formatted_tasks:
yield {
"prompt": [
{"role": "system", "content": "You are a Python developer. Solve this problem. Return ONLY the Python code for main.py, no markdown fences."},
{"role": "user", "content": f"## Problem\n{t['brief']}\n\n## Rules\n- Write your solution in a single `main.py`\n- All functions must have type hints\n- Code must pass ruff, mypy, and pytest"}
],
"task_meta": t
}
return datasets.Dataset.from_generator(gen)
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