# Source code for nlp_architect.data.cdc_resources.gen_scripts.create_word_embed_elmo_dump

    # ******************************************************************************
    # Copyright 2017-2018 Intel Corporation
    #
    # Licensed under the Apache License, Version 2.0 (the "License");
    # you may not use this file except in compliance with the License.
    # You may obtain a copy of the License at
    #
    #     http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing, software
    # distributed under the License is distributed on an "AS IS" BASIS,
    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    # See the License for the specific language governing permissions and
    # limitations under the License.
    # ******************************************************************************
    import argparse
    import logging
    import os
    import pickle
    from os.path import join

    from nlp_architect.common.cdc.mention_data import MentionData
    from nlp_architect.data.cdc_resources.embedding.embed_elmo import ElmoEmbedding
    from nlp_architect.utils import io

    logger = logging.getLogger(__name__)

    def load_elmo_for_vocab(mentions):
        """
        Create the embedding using the cache logic in the embedding class
        Args:
            mentions:

        Returns:

        """
        elmo_embeddings = ElmoEmbedding()

        for mention in mentions:
            elmo_embeddings.get_head_feature_vector(mention)

        logger.info("Total words/contexts in vocabulary %d", len(elmo_embeddings.cache))
        return elmo_embeddings.cache

    def elmo_dump():
        out_file = args.output
        mention_files = list()
        if os.path.isdir(args.mentions):
            for (dirpath, _, files) in os.walk(args.mentions):
                for file in files:
                    if file == ".DS_Store":
                        continue

                    mention_files.append(join(dirpath, file))
        else:
            mention_files.append(args.mentions)

        mentions = []
        for _file in mention_files:
            mentions.extend(MentionData.read_mentions_json_to_mentions_data_list(_file))

        elmo_ecb_embeddings = load_elmo_for_vocab(mentions)

        with open(out_file, "wb") as f:
            pickle.dump(elmo_ecb_embeddings, f)

        logger.info("Saving dump to file-%s", out_file)

    if __name__ == "__main__":
        parser = argparse.ArgumentParser(description="Create Elmo Embedding dataset only dump")
        parser.add_argument("--mentions", type=str, help="mentions_file file", required=True)
        parser.add_argument(
            "--output", type=str, help="location were to create dump file", required=True
        )

        args = parser.parse_args()

        if os.path.isdir(args.mentions):
            io.validate_existing_directory(args.mentions)
        else:
            io.validate_existing_filepath(args.mentions)

        elmo_dump()
        print("Done!")