Read pre-trained word vectors from an eventually compressed text file, possibly contained
    inside an archive with multiple files. The text file is assumed to be utf-8 encoded with
    space-separated fields: [word] [dim 1] [dim 2] ...

    Lines that contain more numerical tokens than ``embedding_dim`` raise a warning and are skipped.

    The remainder of the docstring is identical to ``_read_pretrained_embeddings_file``.

def _read_embeddings_from_text_file(file_uri: str,
                                    embedding_dim: int,
                                    vocab: Vocabulary,
                                    namespace: str = "tokens") -> torch.FloatTensor:
    """
    Read pre-trained word vectors from an eventually compressed text file, possibly contained
    inside an archive with multiple files. The text file is assumed to be utf-8 encoded with
    space-separated fields: [word] [dim 1] [dim 2] ...

    Lines that contain more numerical tokens than ``embedding_dim`` raise a warning and are skipped.

    The remainder of the docstring is identical to ``_read_pretrained_embeddings_file``.
    """
    tokens_to_keep = set(vocab.get_index_to_token_vocabulary(namespace).values())
    vocab_size = vocab.get_vocab_size(namespace)
    embeddings = {}

    # First we read the embeddings from the file, only keeping vectors for the words we need.
    logger.info("Reading pretrained embeddings from file")

    with EmbeddingsTextFile(file_uri) as embeddings_file:
        for line in Tqdm.tqdm(embeddings_file):
            token = line.split(' ', 1)[0]
            if token in tokens_to_keep:
                fields = line.rstrip().split(' ')
                if len(fields) - 1 != embedding_dim:
                    # Sometimes there are funny unicode parsing problems that lead to different
                    # fields lengths (e.g., a word with a unicode space character that splits
                    # into more than one column).  We skip those lines.  Note that if you have
                    # some kind of long header, this could result in all of your lines getting
                    # skipped.  It's hard to check for that here; you just have to look in the
                    # embedding_misses_file and at the model summary to make sure things look
                    # like they are supposed to.
                    logger.warning("Found line with wrong number of dimensions (expected: %d; actual: %d): %s",
                                   embedding_dim, len(fields) - 1, line)
                    continue

                vector = numpy.asarray(fields[1:], dtype='float32')
                embeddings[token] = vector

    if not embeddings:
        raise ConfigurationError("No embeddings of correct dimension found; you probably "
                                 "misspecified your embedding_dim parameter, or didn't "
                                 "pre-populate your Vocabulary")

    all_embeddings = numpy.asarray(list(embeddings.values()))
    embeddings_mean = float(numpy.mean(all_embeddings))
    embeddings_std = float(numpy.std(all_embeddings))
    # Now we initialize the weight matrix for an embedding layer, starting with random vectors,
    # then filling in the word vectors we just read.
    logger.info("Initializing pre-trained embedding layer")
    embedding_matrix = torch.FloatTensor(vocab_size, embedding_dim).normal_(embeddings_mean,
                                                                            embeddings_std)
    num_tokens_found = 0
    index_to_token = vocab.get_index_to_token_vocabulary(namespace)
    for i in range(vocab_size):
        token = index_to_token[i]

        # If we don't have a pre-trained vector for this word, we'll just leave this row alone,
        # so the word has a random initialization.
        if token in embeddings:
            embedding_matrix[i] = torch.FloatTensor(embeddings[token])
            num_tokens_found += 1
        else:
            logger.debug("Token %s was not found in the embedding file. Initialising randomly.", token)

    logger.info("Pretrained embeddings were found for %d out of %d tokens",
                num_tokens_found, vocab_size)

    return embedding_matrix