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Dataset for custom fine-tuning pairs (JSON or JSONL).
Expected fields: prompt, code, optional language.
"""
from __future__ import annotations
import json
from pathlib import Path
from typing import Dict, List
from torch.utils.data import Dataset
from src.tokenizer.code_tokenizer import CodeTokenizer
class CustomPairDataset(Dataset):
def __init__(self, path: str, tokenizer: CodeTokenizer, max_seq_len: int = 512) -> None:
self.path = Path(path)
if not self.path.exists():
raise FileNotFoundError(f"Custom fine-tune data file not found: {self.path}")
self.tokenizer = tokenizer
self.max_seq_len = max_seq_len
self.rows: List[List[int]] = []
self._load()
def _load(self) -> None:
if self.path.suffix.lower() == ".jsonl":
data = []
for line in self.path.read_text(encoding="utf-8-sig").splitlines():
line = line.strip().lstrip("\ufeff")
if not line:
continue
data.append(json.loads(line))
elif self.path.suffix.lower() == ".json":
raw = json.loads(self.path.read_text(encoding="utf-8-sig"))
if isinstance(raw, dict) and "data" in raw:
data = raw["data"]
elif isinstance(raw, list):
data = raw
else:
raise ValueError("JSON fine-tune file must be a list or {'data': [...]}.")
else:
raise ValueError("Custom fine-tune file must be .json or .jsonl")
for row in data:
prompt = str(row.get("prompt", "")).strip()
code = str(row.get("code", "")).strip()
language = str(row.get("language", "python")).strip().lower() or "python"
if not prompt or not code:
continue
text = self.tokenizer.format_training_sample(prompt=prompt, code=code, language=language)
ids = self.tokenizer.encode(text)[: self.max_seq_len]
if len(ids) >= 8:
self.rows.append(ids)
if not self.rows:
raise ValueError("No valid samples found in custom fine-tune data.")
def __len__(self) -> int:
return len(self.rows)
def __getitem__(self, idx: int) -> List[int]:
return self.rows[idx]
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