def tokenize(texts, context_length=77, truncate=False):
    """
    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

    truncate: bool
        Whether to truncate the text in case its encoding is longer than the context length

    Returns
    -------
    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
    We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
    """
    if isinstance(texts, str):
        texts = [texts]
    _tokenizer = SimpleTokenizer()
    sot_token = _tokenizer.encoder["<|startoftext|>"]
    eot_token = _tokenizer.encoder["<|endoftext|>"]
    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token]
                  for text in texts]

    result = paddle.zeros((len(all_tokens), context_length), dtype='int64')

    for i, tokens in enumerate(all_tokens):
        if len(tokens) > context_length:
            if truncate:
                tokens = tokens[:context_length]
                tokens[-1] = eot_token
            else:
                raise RuntimeError(
                    f"Input {texts[i]} is too long for context length {context_length}"
                )
        result[i, :len(tokens)] = paddle.to_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

    truncate: bool
        Whether to truncate the text in case its encoding is longer than the context length

    Returns
    -------
    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
    We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
    
contrib/SegmentAnything/segment_anything/utils/sample_tokenizer.py