""" An automatically created Slovene thesaurus. """
import logging
import os
import xml.etree.ElementTree as ET

import datasets

_CITATION = """\
@article{krek2017translation,
  title={From translation equivalents to synonyms: creation of a Slovene thesaurus using word co-occurrence network analysis},
  author={Krek, Simon and Laskowski, Cyprian and Robnik-{\v{S}}ikonja, Marko},
  journal={Proceedings of eLex},
  pages={93--109},
  year={2017}
}
"""

_DESCRIPTION = """\
This is an automatically created Slovene thesaurus from Slovene data available in a comprehensive 
English–Slovenian dictionary, a monolingual dictionary, and a corpus. A network analysis on the bilingual dictionary 
word co-occurrence graph was used, together with additional information from the distributional thesaurus data 
available as part of the Sketch Engine tool and extracted from the 1.2 billion word Gigafida corpus and the 
monolingual dictionary.
"""

_HOMEPAGE = "http://hdl.handle.net/11356/1166"

_LICENSE = "Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)"

_URLS = {
    "slo_thesaurus": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1166/CJVT_Thesaurus-v1.0.zip",
}


class SloThesaurus(datasets.GeneratorBasedBuilder):
    """An automatically created Slovene thesaurus."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = datasets.Features(
            {
                "id_headword": datasets.Value("string"),
                "headword": datasets.Value("string"),
                "groups_core": [
                    {
                        "id_words": datasets.Sequence(datasets.Value("string")),
                        "words": datasets.Sequence(datasets.Value("string")),
                        "scores": datasets.Sequence(datasets.Value("float32")),
                        "domains": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
                    }
                ],
                "groups_near": [
                    {
                        "id_words": datasets.Sequence(datasets.Value("string")),
                        "words": datasets.Sequence(datasets.Value("string")),
                        "scores": datasets.Sequence(datasets.Value("float32")),
                        "domains": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
                    }
                ]
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        urls = _URLS["slo_thesaurus"]
        data_dir = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"file_path": os.path.join(data_dir, "CJVT_Thesaurus-v1.0.xml")}
            )
        ]

    def _generate_examples(self, file_path):
        curr_doc = ET.parse(file_path)
        root = curr_doc.getroot()

        for idx_entry, curr_entry in enumerate(root.iterfind(f".//entry")):
            head_word = curr_entry.find("headword")
            if head_word is None:
                logging.warning("<headword> is missing for an entry, which should likely not happen. "
                                "Please open an issue on the dataset repository if you are seeing this.")
                head_word = {"text": "UNK_headword", "id": "NA_id"}
            else:
                head_word = {"text": head_word.text.strip(), "id": head_word.attrib["id"]}

            all_core_groups = []
            core_groups = curr_entry.find("groups_core")
            if core_groups is not None:
                for idx_group, core_group in enumerate(core_groups.iterfind("group"), start=0):
                    parsed_group = {"id_words": [], "words": [], "scores": [], "domains": []}
                    all_candidates = core_group.iterfind("candidate")
                    for candidate in all_candidates:
                        candidate_s = candidate.find("s")
                        candidate_domains = candidate.find("labels")
                        if candidate_domains is not None:
                            candidate_domains = list(map(lambda candidate_el: candidate_el.text.strip(),
                                                         candidate_domains.findall("la")))
                        else:
                            candidate_domains = []

                        parsed_group["id_words"].append(candidate_s.attrib["id"])
                        parsed_group["words"].append(candidate_s.text.strip())
                        parsed_group["scores"].append(float(candidate.attrib["score"]))
                        parsed_group["domains"].append(candidate_domains)

                    all_core_groups.append(parsed_group)

            all_near_groups = []
            near_groups = curr_entry.find("groups_near")
            if near_groups is not None:
                for idx_group, core_group in enumerate(near_groups.iterfind("group"), start=0):
                    parsed_group = {"id_words": [], "words": [], "scores": [], "domains": []}
                    all_candidates = core_group.iterfind("candidate")
                    for candidate in all_candidates:
                        candidate_s = candidate.find("s")
                        candidate_domains = candidate.find("labels")
                        if candidate_domains is not None:
                            candidate_domains = list(map(lambda candidate_el: candidate_el.text.strip(),
                                                         candidate_domains.findall("la")))
                        else:
                            candidate_domains = []

                        parsed_group["id_words"].append(candidate_s.attrib["id"])
                        parsed_group["words"].append(candidate_s.text.strip())
                        parsed_group["scores"].append(float(candidate.attrib["score"]))
                        parsed_group["domains"].append(candidate_domains)

                    all_near_groups.append(parsed_group)

            yield idx_entry, {
                "id_headword": head_word["id"],
                "headword": head_word["text"],
                "groups_core": all_core_groups,
                "groups_near": all_near_groups
            }