def tokenize(
    texts: Union[str, List[str]], context_length: int = 77
) -> torch.LongTensor:
    """
    Returns the tokenized representation of given input string(s)

    Parameters
    ----------
    texts : Union[str, List[str]]
        An input string or a list of input strings to tokenize
    context_length : int
        The context length to use; all CLIP models use 77 as the context length

    Returns
    -------
    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
    """
    if isinstance(texts, str):
        texts = [texts]

    sot_token = _tokenizer.encoder["<start_of_text>"]
    eot_token = _tokenizer.encoder["<end_of_text>"]
    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
    result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)

    for i, tokens in enumerate(all_tokens):
        if len(tokens) > context_length:
            tokens = tokens[:context_length]  # Truncate
        result[i, : len(tokens)] = torch.tensor(tokens)

    return result
Returns the tokenized representation of given input string(s)

Parameters
----------
texts : Union[str, List[str]]
An input string or a list of input strings to tokenize
context_length : int
The context length to use; all CLIP models use 77 as the context length

Returns
-------
A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
models/tta/picoaudio/picoaudio/audioldm/clap/open_clip/tokenizer.py