# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# Lint as: python3
"""NKJP-POS tagging dataset."""

import csv
from typing import List, Tuple, Dict, Generator

import datasets

_DESCRIPTION = """NKJP-POS tagging dataset."""

_URLS = {
    "train": "https://huggingface.co/datasets/clarin-pl/nkjp-pos/resolve/main/data/train.tsv",
    "validation": "https://huggingface.co/datasets/clarin-pl/nkjp-pos/resolve/main/data/valid.tsv",
    "test": "https://huggingface.co/datasets/clarin-pl/nkjp-pos/resolve/main/data/test.tsv",
}

_HOMEPAGE = "http://clip.ipipan.waw.pl/NationalCorpusOfPolish"

_POS_TAGS = [
    'adj',
    'adja',
    'adjc',
    'adjp',
    'adv',
    'aglt',
    'bedzie',
    'brev',
    'burk',
    'comp',
    'conj',
    'depr',
    'fin',
    'ger',
    'imps',
    'impt',
    'inf',
    'interj',
    'interp',
    'num',
    'numcol',
    'pact',
    'pant',
    'pcon',
    'ppas',
    'ppron12',
    'ppron3',
    'praet',
    'pred',
    'prep',
    'qub',
    'siebie',
    'subst',
    'winien',
    'xxx'
]


class NKJPPOS(datasets.GeneratorBasedBuilder):

    def _info(self) -> datasets.DatasetInfo:
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "morph": datasets.Sequence(datasets.Value("string")),
                    "lemmas": datasets.Sequence(datasets.Value("string")),
                    "pos_tags": datasets.Sequence(datasets.features.ClassLabel(
                        names=_POS_TAGS,
                        num_classes=len(_POS_TAGS)
                    )),
                    "full_pos_tags": datasets.Sequence(
                        datasets.Value("string")),
                    "nps": datasets.Sequence(datasets.Value("string")),
                }
            ),
            homepage=_HOMEPAGE,
        )

    def _split_generators(
            self, dl_manager: datasets.DownloadManager
    ) -> List[datasets.SplitGenerator]:
        urls_to_download = _URLS
        downloaded_files = dl_manager.download_and_extract(urls_to_download)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": downloaded_files["train"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": downloaded_files["validation"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"filepath": downloaded_files["test"]},
            ),
        ]

    @staticmethod
    def _parse_tag(
            tag: str
    ) -> Tuple[str, str]:
        full_tag = tag
        pos_tag = tag.split(':')[0]

        return pos_tag, full_tag

    def _generate_examples(
            self, filepath: str
    ) -> Generator[Tuple[int, Dict[str, str]], None, None]:
        with open(filepath, 'r', encoding="utf-8") as f:
            reader = csv.reader(f, delimiter='\t', quoting=csv.QUOTE_NONE)

            tokens = []
            morph = []
            tags = []
            full_tags = []
            lemma = []
            nps = []
            gid = 0

            for line in reader:
                if not line:
                    yield gid, {
                        'tokens': tokens,
                        'morph': morph,
                        'pos_tags': tags,
                        'full_pos_tags': full_tags,
                        'lemmas': lemma,
                        'nps': nps
                    }
                    gid += 1
                    tokens = []
                    morph = []
                    tags = []
                    full_tags = []
                    lemma = []
                    nps = []

                else:
                    tokens.append(line[0])
                    morph.append(line[1])
                    lemma.append(line[3])
                    nps.append(line[4])
                    tag, full_tag = self._parse_tag(line[2])

                    tags.append(tag)
                    full_tags.append(full_tag)