from tokenizers import Tokenizer
from transformers import GPT2Tokenizer, GPT2TokenizerFast

def fetch_encoder(params):
    no_dataset = params.get('no_dataset', False)
    if no_dataset:
        return None

    dataset = next(iter(params['dataset_configs'].values())) # Get the first value from the dict
    path = dataset["tokenizer_path"]
    is_pretrained = dataset.get("tokenizer_is_pretrained", False)

    if is_pretrained:
        tok = GPT2TokenizerFast.from_pretrained(path)

        # Will add a padding token id of 50257 at run-time
        tok.add_special_tokens({'pad_token': '<|padding|>'})
        return tok

    return Tokenizer.from_file(path)


# GPT2Tokenizer and Tokenizer have different ways of fetching token ids
def encode(encoder, text, gpt=True):
    result = encoder.encode(text)
    if isinstance(result, list):
        return result
    return result.ids