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File: //proc/1233/cwd/usr/local/lib/python3.10/dist-packages/tiktoken/__pycache__/core.cpython-310.pyc
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dS)awCreates an Encoding object.

        See openai_public.py for examples of how to construct an Encoding object.

        Args:
            name: The name of the encoding. It should be clear from the name of the encoding
                what behaviour to expect, in particular, encodings with different special tokens
                should have different names.
            pat_str: A regex pattern string that is used to split the input text.
            mergeable_ranks: A dictionary mapping mergeable token bytes to their ranks. The ranks
                must correspond to merge priority.
            special_tokens: A dictionary mapping special token strings to their token values.
            explicit_n_vocab: The number of tokens in the vocabulary. If provided, it is checked
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dd�}|j�||�YSw)aHEncodes a string into tokens.

        Special tokens are artificial tokens used to unlock capabilities from a model,
        such as fill-in-the-middle. So we want to be careful about accidentally encoding special
        tokens, since they can be used to trick a model into doing something we don't want it to do.

        Hence, by default, encode will raise an error if it encounters text that corresponds
        to a special token. This can be controlled on a per-token level using the `allowed_special`
        and `disallowed_special` parameters. In particular:
        - Setting `disallowed_special` to () will prevent this function from raising errors and
          cause all text corresponding to special tokens to be encoded as natural text.
        - Setting `allowed_special` to "all" will cause this function to treat all text
          corresponding to special tokens to be encoded as special tokens.

        ```
        >>> enc.encode("hello world")
        [31373, 995]
        >>> enc.encode("<|endoftext|>", allowed_special={"<|endoftext|>"})
        [50256]
        >>> enc.encode("<|endoftext|>", allowed_special="all")
        [50256]
        >>> enc.encode("<|endoftext|>")
        # Raises ValueError
        >>> enc.encode("<|endoftext|>", disallowed_special=())
        [27, 91, 437, 1659, 5239, 91, 29]
        ```
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        This is equivalent to `encode_batch(text, disallowed_special=())` (but slightly faster).

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        See `encode` for more details on `allowed_special` and `disallowed_special`.

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$�zEncoding.encode_batch�!tuple[list[int], list[list[int]]]cCsb|dkr|j}|dkr|j|}|r*t|t�st|�}t|��|�}r*t|���|j�||�S)a�Encodes a string into stable tokens and possible completion sequences.

        Note that the stable tokens will only represent a substring of `text`.

        See `encode` for more details on `allowed_special` and `disallowed_special`.

        This API should itself be considered unstable.

        ```
        >>> enc.encode_with_unstable("hello fanta")
        ([31373], [(277, 4910), (5113, 265), ..., (8842,)])

        >>> text = "..."
        >>> stable_tokens, completions = enc.encode_with_unstable(text)
        >>> assert text.encode().startswith(enc.decode_bytes(stable_tokens))
        >>> assert all(enc.decode_bytes(stable_tokens + seq).startswith(text.encode()) for seq in completions)
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        of the first character that contains bytes from the token.

        This will currently raise if given tokens that decode to invalid UTF-8; this behaviour may
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