import os
import re
import xml.etree.ElementTree as ET

import datasets


_CITATION = """\
@misc{janes_tag,
    title = {{CMC} training corpus Janes-Tag 3.0},
    author = {Lenardi{\v c}, Jakob and {\v C}ibej, Jaka and Arhar Holdt, {\v S}pela and Erjavec, Toma{\v z} and Fi{\v s}er, Darja and Ljube{\v s}i{\'c}, Nikola and Zupan, Katja and Dobrovoljc, Kaja},
    url = {http://hdl.handle.net/11356/1732},
    note = {Slovenian language resource repository {CLARIN}.{SI}},
    copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
    year = {2022}
}
"""


_DESCRIPTION = """\
Janes-Tag is a manually annotated corpus of Slovene Computer-Mediated Communication (CMC) consisting of mostly tweets 
but also blogs, forums and news comments.
"""

_HOMEPAGE = "https://nl.ijs.si/janes/"

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

_URLS = {
    "janes_tag_tei": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1732/Janes-Tag.3.0.TEI.zip"
}

XML_NAMESPACE = "{http://www.w3.org/XML/1998/namespace}"
DEFAULT_NE = "O"


def namespace(element):
    # https://stackoverflow.com/a/12946675
    m = re.match(r'\{.*\}', element.tag)
    return m.group(0) if m else ''


def word_info(wordlike_tag, _namespace):
    if wordlike_tag.tag == f"{_namespace}c":
        return None, None, None, None

    if wordlike_tag.tag in {f"{_namespace}w", f"{_namespace}pc"}:
        nes = None

        children = list(iter(wordlike_tag))
        if len(children) > 0:
            # If this happens, the word contains nested words indicating its normalized form
            words, lemmas, msds = [], [], []
            for _child in wordlike_tag:
                assert _child.tag in {f"{_namespace}w", f"{_namespace}pc"}, _child.tag

                # Arbitrary words in the text have a normalized form that is formatted inconsistently and so it is
                # unclear how to parse it correctly -> convention: always use information of the normalized words
                if "norm" in _child.attrib:
                    words.append(_child.attrib["norm"].strip())
                    lemmas.append(_child.attrib["lemma"].strip())
                    msds.append(_child.attrib["ana"].strip())
                else:
                    # These don't have linguistic annotations ¯\_(ツ)_/¯
                    words.append(_child.text.strip())
                    lemmas.append(_child.text.strip())
                    msds.append("UNK")

        else:
            words = [wordlike_tag.text.strip()]
            lemmas = [wordlike_tag.attrib["lemma"].strip()]
            msds = [wordlike_tag.attrib["ana"].strip()]

        return words, lemmas, msds, nes

    words, lemmas, msds, nes = [], [], [], []

    if wordlike_tag.tag == f"{_namespace}seg":
        ne_tag = wordlike_tag.attrib["subtype"].strip().upper()
        if ne_tag.startswith("DERIV-"):
            ne_tag = ne_tag[len("DERIV-"):]

        for _child in wordlike_tag:
            _child_words, _child_lemmas, _child_msds, _child_nes = word_info(_child, _namespace)

            if _child_words is None:
                continue

            words.extend(_child_words)
            lemmas.extend(_child_lemmas)
            msds.extend(_child_msds)

        nes = [f"B-{ne_tag}" if _i == 0 else f"I-{ne_tag}" for _i, _ in enumerate(words)]

        return words, lemmas, msds, nes


class JanesTag(datasets.GeneratorBasedBuilder):
    """Janes-Tag is a manually annotated corpus of Slovene Computer-Mediated Communication"""

    VERSION = datasets.Version("3.0.0")

    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "words": datasets.Sequence(datasets.Value("string")),
                "lemmas": datasets.Sequence(datasets.Value("string")),
                "msds": datasets.Sequence(datasets.Value("string")),
                "nes": 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["janes_tag_tei"]
        data_dir = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"file_path": os.path.join(data_dir, "Janes-Tag.3.0.TEI", "janes-tag.xml")}
            )
        ]

    def _generate_examples(self, file_path):
        curr_doc = ET.parse(file_path)
        root = curr_doc.getroot()
        NAMESPACE = namespace(root)
        root = root.find(f"{NAMESPACE}text").find(f"{NAMESPACE}body")

        idx_ex = 0
        for curr_ex in root.iterfind(f"{NAMESPACE}ab"):  # anonymous block
            curr_id = curr_ex.attrib[f"{XML_NAMESPACE}id"]
            ex_words, ex_lemmas, ex_msds, ex_nes = [], [], [], []
            for child_tag in curr_ex:
                if child_tag.tag not in {f"{NAMESPACE}s", f"{NAMESPACE}c"}:
                    continue

                if child_tag.tag == f"{NAMESPACE}c":
                    continue

                # Iterate over elements of a <s>entence
                for word_or_seg_tag in child_tag:
                    _words, _lemmas, _msds, _nes = word_info(word_or_seg_tag, NAMESPACE)

                    if _words is None:
                        continue

                    if _nes is None:
                        _nes = [DEFAULT_NE for _ in range(len(_words))]

                    ex_words.extend(_words)
                    ex_lemmas.extend(_lemmas)
                    ex_msds.extend(_msds)
                    ex_nes.extend(_nes)

            yield idx_ex, {
                "id": curr_id,
                "words": ex_words,
                "lemmas": ex_lemmas,
                "msds": ex_msds,
                "nes": ex_nes
            }
            idx_ex += 1