Creates ELMo word representations from a vocabulary file. These
    word representations are _independent_ - they are the result of running
    the CNN and Highway layers of the ELMo model, but not the Bidirectional LSTM.
    ELMo requires 2 additional tokens: <S> and </S>. The first token
    in this file is assumed to be an unknown token.

    This script produces two artifacts: A new vocabulary file
    with the <S> and </S> tokens inserted and a glove formatted embedding
    file containing word : vector pairs, one per line, with all values
    separated by a space.

def main(vocab_path: str,
         elmo_config_path: str,
         elmo_weights_path: str,
         output_dir: str,
         batch_size: int,
         device: int,
         use_custom_oov_token: bool = False):
    """
    Creates ELMo word representations from a vocabulary file. These
    word representations are _independent_ - they are the result of running
    the CNN and Highway layers of the ELMo model, but not the Bidirectional LSTM.
    ELMo requires 2 additional tokens: <S> and </S>. The first token
    in this file is assumed to be an unknown token.

    This script produces two artifacts: A new vocabulary file
    with the <S> and </S> tokens inserted and a glove formatted embedding
    file containing word : vector pairs, one per line, with all values
    separated by a space.
    """

    # Load the vocabulary words and convert to char ids
    with open(vocab_path, 'r') as vocab_file:
        tokens = vocab_file.read().strip().split('\n')

    # Insert the sentence boundary tokens which elmo uses at positions 1 and 2.
    if tokens[0] != DEFAULT_OOV_TOKEN and not use_custom_oov_token:
        raise ConfigurationError("ELMo embeddings require the use of a OOV token.")

    tokens = [tokens[0]] + ["<S>", "</S>"] + tokens[1:]

    indexer = ELMoTokenCharactersIndexer()
    indices = indexer.tokens_to_indices([Token(token) for token in tokens], Vocabulary(), "indices")["indices"]
    sentences = []
    for k in range((len(indices) // 50) + 1):
        sentences.append(indexer.pad_token_sequence(indices[(k * 50):((k + 1) * 50)],
                                                    desired_num_tokens=50,
                                                    padding_lengths={}))

    last_batch_remainder = 50 - (len(indices) % 50)
    if device != -1:
        elmo_token_embedder = _ElmoCharacterEncoder(elmo_config_path,
                                                    elmo_weights_path).cuda(device)
    else:
        elmo_token_embedder = _ElmoCharacterEncoder(elmo_config_path,
                                                    elmo_weights_path)

    all_embeddings = []
    for i in range((len(sentences) // batch_size) + 1):
        array = numpy.array(sentences[i * batch_size: (i + 1) * batch_size])
        if device != -1:
            batch = torch.from_numpy(array).cuda(device)
        else:
            batch = torch.from_numpy(array)

        token_embedding = elmo_token_embedder(batch)['token_embedding'].data

        # Reshape back to a list of words of shape (batch_size * 50, encoding_dim)
        # We also need to remove the <S>, </S> tokens appended by the encoder.
        per_word_embeddings = token_embedding[:, 1:-1, :].contiguous().view(-1, token_embedding.size(-1))

        all_embeddings.append(per_word_embeddings)

    # Remove the embeddings associated with padding in the last batch.
    all_embeddings[-1] = all_embeddings[-1][:-last_batch_remainder, :]

    embedding_weight = torch.cat(all_embeddings, 0).cpu().numpy()

    # Write out the embedding in a glove format.
    os.makedirs(output_dir, exist_ok=True)
    with gzip.open(os.path.join(output_dir, "elmo_embeddings.txt.gz"), 'wb') as embeddings_file:
        for i, word in enumerate(tokens):
            string_array = " ".join([str(x) for x in list(embedding_weight[i, :])])
            embeddings_file.write(f"{word} {string_array}\n".encode('utf-8'))

    # Write out the new vocab with the <S> and </S> tokens.
    _, vocab_file_name = os.path.split(vocab_path)
    with open(os.path.join(output_dir, vocab_file_name), "w") as new_vocab_file:
        for word in tokens:
            new_vocab_file.write(f"{word}\n")