# 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.
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#
#     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 json
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.jsonl",
    "test": "https://huggingface.co/datasets/clarin-pl/nkjp-pos/resolve/main/data/test.jsonl",
}

_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):
    VERSION = datasets.Version("1.1.0")

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

    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.TEST,
                gen_kwargs={"filepath": downloaded_files["test"]},
            ),
        ]

    @staticmethod
    def _clean_line(data_line: Dict):
        new_tokens = []
        new_pos_tags = []
        for token, pos_tag in zip(data_line["tokens"], data_line["pos_tags"]):
            if pos_tag in _POS_TAGS:
                new_tokens.append(token)
                new_pos_tags.append(pos_tag)
        data_line["tokens"] = new_tokens
        data_line["pos_tags"] = new_pos_tags
        assert len(data_line["tokens"]) == len(data_line["pos_tags"])
        return data_line

    def _generate_examples(
        self, filepath: str
    ) -> Generator[Tuple[str, Dict[str, str]], None, None]:
        with open(filepath, "r", encoding="utf-8") as f:
            for line in f:
                data_line = self._clean_line(json.loads(line))
                yield data_line["id"], data_line