from itertools import chain
from typing import TYPE_CHECKING, Any, Dict, List


if TYPE_CHECKING:
    from transformers.tokenization_utils import PreTrainedTokenizer

    from ...hparams import DataArguments


def preprocess_pretrain_dataset(
    examples: Dict[str, List[Any]], tokenizer: "PreTrainedTokenizer", data_args: "DataArguments"
) -> Dict[str, List[List[int]]]:
    # build grouped texts with format `X1 X2 X3 ...` if packing is enabled
    eos_token = "<|end_of_text|>" if data_args.template == "llama3" else tokenizer.eos_token
    text_examples = [messages[0]["content"] + eos_token for messages in examples["prompt"]]

    if not data_args.packing:
        if data_args.template == "gemma":
            text_examples = [tokenizer.bos_token + example for example in text_examples]

        result = tokenizer(text_examples, add_special_tokens=False, max_length=data_args.cutoff_len, truncation=True)
    else:
        tokenized_examples = tokenizer(text_examples, add_special_tokens=False)
        concatenated_examples = {k: list(chain(*tokenized_examples[k])) for k in tokenized_examples.keys()}
        total_length = len(concatenated_examples[list(concatenated_examples.keys())[0]])
        block_size = data_args.cutoff_len
        total_length = (total_length // block_size) * block_size
        result = {
            k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
            for k, t in concatenated_examples.items()
        }
        if data_args.template == "gemma":
            for i in range(len(result["input_ids"])):
                result["input_ids"][i][0] = tokenizer.bos_token_id

    return result