# Source code for nlp_architect.data.cdc_resources.relations.verbocean_relation_extraction

    # ******************************************************************************
    # 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 logging
    import os
    from typing import Dict, Set

    from nlp_architect.common.cdc.mention_data import MentionDataLight
    from nlp_architect.data.cdc_resources.relations.relation_extraction import RelationExtraction
    from nlp_architect.data.cdc_resources.relations.relation_types_enums import (
        RelationType,
        OnlineOROfflineMethod,
    )
    from nlp_architect.utils.io import load_json_file
    from nlp_architect.utils.string_utils import StringUtils

    logger = logging.getLogger(__name__)

    class VerboceanRelationExtraction(RelationExtraction):
        def __init__(
            self, method: OnlineOROfflineMethod = OnlineOROfflineMethod.ONLINE, vo_file: str = None
        ):
            """
            Extract Relation between two mentions according to VerbOcean knowledge

            Args:
                method (optional): OnlineOROfflineMethod.{ONLINE/OFFLINE} run against full VerbOcean or
                    a sub-set of it (default = ONLINE)
                vo_file (required): str Location of VerbOcean file to work with
            """
            logger.info("Loading Verb Ocean module")
            if vo_file is not None and os.path.isfile(vo_file):
                if method == OnlineOROfflineMethod.OFFLINE:
                    self.vo = load_json_file(vo_file)
                elif method == OnlineOROfflineMethod.ONLINE:
                    self.vo = self.load_verbocean_file(vo_file)
                logger.info("Verb Ocean module lead successfully")
            else:
                raise FileNotFoundError("VerbOcean file not found or not in path..")
            super(VerboceanRelationExtraction, self).__init__()

        def extract_all_relations(
            self, mention_x: MentionDataLight, mention_y: MentionDataLight
        ) -> Set[RelationType]:
            ret_ = set()
            ret_.add(self.extract_sub_relations(mention_x, mention_y, RelationType.VERBOCEAN_MATCH))
            return ret_

        def extract_sub_relations(
            self, mention_x: MentionDataLight, mention_y: MentionDataLight, relation: RelationType
        ) -> RelationType:
            """
            Check if input mentions has the given relation between them

            Args:
                mention_x: MentionDataLight
                mention_y: MentionDataLight
                relation: RelationType

            Returns:
                RelationType: relation in case mentions has given relation or
                    RelationType.NO_RELATION_FOUND otherwise
            """
            if relation is not RelationType.VERBOCEAN_MATCH:
                return RelationType.NO_RELATION_FOUND

            mention_x_str = mention_x.tokens_str
            mention_y_str = mention_y.tokens_str
            if StringUtils.is_pronoun(mention_x_str.lower()) or StringUtils.is_pronoun(
                mention_y_str.lower()
            ):
                return RelationType.NO_RELATION_FOUND

            if self.is_verbocean_relation(mention_x, mention_y):
                return RelationType.VERBOCEAN_MATCH

            return RelationType.NO_RELATION_FOUND

        def is_verbocean_relation(
            self, mention_x: MentionDataLight, mention_y: MentionDataLight
        ) -> bool:
            """
            Check if input mentions has VerbOcean relation between them

            Args:
                mention_x: MentionDataLight
                mention_y: MentionDataLight

            Returns:
                bool
            """
            x_head = mention_x.mention_head

            y_head = mention_y.mention_head

            rel = None

            if x_head in self.vo and y_head in self.vo[x_head]:
                rel = self.vo[x_head][y_head]
            elif y_head in self.vo and x_head in self.vo[y_head]:
                rel = self.vo[y_head][x_head]

            match_result = False
            if rel is not None and rel != "[unk]" and rel != "[low-vol]":
                match_result = True

            return match_result

        @staticmethod
        def get_supported_relations():
            """
            Return all supported relations by this class

            Returns:
                List[RelationType]
            """
            return [RelationType.VERBOCEAN_MATCH]

        @staticmethod
        def load_verbocean_file(fname: str) -> Dict[str, Dict[str, str]]:
            """
            Method to load referent dictionary to memory

            Returns:
                List[RelationType]
            """
            word_dict = {}
            with open(fname) as f:
                for line in f:
                    word1, rel, word2, _, _ = line.strip().split()
                    if word1 not in word_dict:
                        word_dict[word1] = {}
                    word_dict[word1][word2] = rel
            return word_dict