"""Base class for HuggingFace-based datasets."""

from typing import Optional, Dict
import torch
from torch.utils.data import Dataset
from datasets import load_dataset
from transformers import AutoTokenizer
from taoTrain.config import TrainingConfig


class BaseHFDataset(Dataset):
    """Base class for HuggingFace-based datasets."""
    
    def __init__(self, config: TrainingConfig, split: str = "train"):
        """
        Initialize dataset.
        
        Args:
            config: Training configuration
            split: Dataset split (train, validation, test)
        """
        self.config = config
        self.split = split
        self.data = None
        self.tokenizer = None
        
        # Load tokenizer
        self._load_tokenizer()
        
        # Load and preprocess dataset
        self._load_dataset()
        self._preprocess()
    
    def _load_tokenizer(self):
        """Load tokenizer from HuggingFace."""
        # Default to GPT-2 tokenizer if not specified
        tokenizer_name = getattr(self.config, 'tokenizer_name', 'gpt2')
        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
        
        # Set pad token if not set
        if self.tokenizer.pad_token is None:
            self.tokenizer.pad_token = self.tokenizer.eos_token
    
    def _load_dataset(self):
        """Load dataset from HuggingFace."""
        dataset_config = self.config.dataset
        
        try:
            # Load dataset
            if dataset_config.config:
                self.data = load_dataset(
                    dataset_config.dataset_name,
                    dataset_config.config,
                    split=self.split,
                    cache_dir=dataset_config.cache_dir,
                    trust_remote_code=True,
                )
            else:
                self.data = load_dataset(
                    dataset_config.dataset_name,
                    split=self.split,
                    cache_dir=dataset_config.cache_dir,
                    trust_remote_code=True,
                )
        except Exception as e:
            raise ValueError(f"Failed to load dataset {dataset_config.dataset_name}: {e}")
        
        # Limit samples if specified
        if dataset_config.max_samples:
            self.data = self.data.select(range(min(dataset_config.max_samples, len(self.data))))
    
    def _preprocess(self):
        """Preprocess dataset (to be implemented by subclasses)."""
        pass
    
    def __len__(self) -> int:
        """Return dataset length."""
        return len(self.data)
    
    def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
        """Get item (to be implemented by subclasses)."""
        pass