    def set_from_file(
        self,
        filename: str,
        is_padded: bool = True,
        oov_token: str = DEFAULT_OOV_TOKEN,
        namespace: str = "tokens",
    ):
        """
        If you already have a vocabulary file for a trained model somewhere, and you really want to
        use that vocabulary file instead of just setting the vocabulary from a dataset, for
        whatever reason, you can do that with this method.  You must specify the namespace to use,
        and we assume that you want to use padding and OOV tokens for this.

        # Parameters

        filename : `str`
            The file containing the vocabulary to load.  It should be formatted as one token per
            line, with nothing else in the line.  The index we assign to the token is the line
            number in the file (1-indexed if `is_padded`, 0-indexed otherwise).  Note that this
            file should contain the OOV token string!
        is_padded : `bool`, optional (default=`True`)
            Is this vocabulary padded?  For token / word / character vocabularies, this should be
            `True`; while for tag or label vocabularies, this should typically be `False`.  If
            `True`, we add a padding token with index 0, and we enforce that the `oov_token` is
            present in the file.
        oov_token : `str`, optional (default=`DEFAULT_OOV_TOKEN`)
            What token does this vocabulary use to represent out-of-vocabulary characters?  This
            must show up as a line in the vocabulary file.  When we find it, we replace
            `oov_token` with `self._oov_token`, because we only use one OOV token across
            namespaces.
        namespace : `str`, optional (default=`"tokens"`)
            What namespace should we overwrite with this vocab file?
        """
        if is_padded:
            self._token_to_index[namespace] = {self._padding_token: 0}
            self._index_to_token[namespace] = {0: self._padding_token}
        else:
            self._token_to_index[namespace] = {}
            self._index_to_token[namespace] = {}
        with codecs.open(filename, "r", "utf-8") as input_file:
            lines = _NEW_LINE_REGEX.split(input_file.read())
            # Be flexible about having final newline or not
            if lines and lines[-1] == "":
                lines = lines[:-1]
            for i, line in enumerate(lines):
                index = i + 1 if is_padded else i
                token = line.replace("@@NEWLINE@@", "\n")
                if token == oov_token:
                    token = self._oov_token
                self._token_to_index[namespace][token] = index
                self._index_to_token[namespace][index] = token
        if is_padded:
            assert self._oov_token in self._token_to_index[namespace], "OOV token not found!"
If you already have a vocabulary file for a trained model somewhere, and you really want to
use that vocabulary file instead of just setting the vocabulary from a dataset, for
whatever reason, you can do that with this method.  You must specify the namespace to use,
and we assume that you want to use padding and OOV tokens for this.

# Parameters

filename : `str`
The file containing the vocabulary to load.  It should be formatted as one token per
line, with nothing else in the line.  The index we assign to the token is the line
number in the file (1-indexed if `is_padded`, 0-indexed otherwise).  Note that this
file should contain the OOV token string!
is_padded : `bool`, optional (default=`True`)
Is this vocabulary padded?  For token / word / character vocabularies, this should be
`True`; while for tag or label vocabularies, this should typically be `False`.  If
`True`, we add a padding token with index 0, and we enforce that the `oov_token` is
present in the file.
oov_token : `str`, optional (default=`DEFAULT_OOV_TOKEN`)
What token does this vocabulary use to represent out-of-vocabulary characters?  This
must show up as a line in the vocabulary file.  When we find it, we replace
`oov_token` with `self._oov_token`, because we only use one OOV token across
namespaces.
namespace : `str`, optional (default=`"tokens"`)
What namespace should we overwrite with this vocab file?
allennlp/data/vocabulary.py