from datasets import load_dataset
from transformers import AutoTokenizer, DataCollatorForLanguageModeling
from torch.utils.data import DataLoader
from typing import Tuple

def build_dataloaders(dataset_name: str, tokenizer_name: str, batch_size: int, val_split: float = 0.05, block_size: int = 512, num_workers: int = 2) -> Tuple:
    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token

    raw = load_dataset(dataset_name)
    if 'train' not in raw:
        raw = {'train': raw}
    if isinstance(raw, dict) and 'train' in raw:
        ds = raw['train']
    else:
        ds = raw

    split = ds.train_test_split(test_size=val_split, seed=42) if hasattr(ds, 'train_test_split') else {'train': ds, 'test': ds}
    train_ds, val_ds = split['train'], split['test']

    def text_key(example):
        for k in example.keys():
            if example[k] is not None and isinstance(example[k], str):
                return k
        return None

    sample = train_ds[0]
    tkey = text_key(sample) or 'text'

    train_tok = train_ds.map(lambda ex: tokenizer(ex[tkey], truncation=True, padding='max_length', max_length=block_size), batched=True, remove_columns=train_ds.column_names)
    val_tok = val_ds.map(lambda ex: tokenizer(ex[tkey], truncation=True, padding='max_length', max_length=block_size), batched=True, remove_columns=val_ds.column_names)

    def labelize(batch):
        input_ids = batch['input_ids']
        labels = [ids[:] for ids in input_ids]
        for i, ids in enumerate(labels):
            labels[i] = [(-100 if token == tokenizer.pad_token_id else token) for token in ids]
        batch['labels'] = labels
        return batch

    train_tok = train_tok.map(labelize, batched=True)
    val_tok = val_tok.map(labelize, batched=True)

    collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

    train_loader = DataLoader(train_tok, batch_size=batch_size, shuffle=True, num_workers=num_workers, collate_fn=collator)
    val_loader = DataLoader(val_tok, batch_size=max(2, batch_size), shuffle=False, num_workers=num_workers, collate_fn=collator)

    return tokenizer, train_loader, val_loader