# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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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"""Croatian web corpus hrWaC 2.1"""

import datasets


_CITATION = """
@misc{11356/1064,
 title = {Croatian web corpus {hrWaC} 2.1},
 author = {Ljube{\v s}i{\'c}, Nikola and Klubi{\v c}ka, Filip},
 url = {http://hdl.handle.net/11356/1064},
 note = {Slovenian language resource repository {CLARIN}.{SI}},
 copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
 year = {2016} }
"""

_DESCRIPTION = """\
The Croatian web corpus hrWaC was built by crawling the .hr top-level domain in 2011 and again in 2014. The corpus was near-deduplicated on paragraph level, normalised via diacritic restoration, morphosyntactically annotated and lemmatised. The corpus is shuffled by paragraphs. Each paragraph contains metadata on the URL, domain and language identification (Croatian vs. Serbian).

Version 2.0 of this corpus is described in http://www.aclweb.org/anthology/W14-0405. Version 2.1 contains newer and better linguistic annotations.
"""

_LICENSE = "CC BY-SA 4.0"

_HOMEPAGE = "http://nlp.ffzg.hr/resources/corpora/hrwac/"
_URLS = [
    f"https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1064/hrWaC2.1.{i + 1:02d}.xml.gz"
    for i in range(14)
]


class Hrwac(datasets.GeneratorBasedBuilder):
    """Croatian web corpus hrWaC"""

    VERSION = datasets.Version("2.1.0")
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="hrwac",
            version=VERSION,
            description="The hrWac dataset.",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "sentence": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_file = dl_manager.download_and_extract(_URLS)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": downloaded_file,
                },
            ),
        ]

    def _generate_examples(self, filepath):
        for file_idx, fp in enumerate(filepath):
            with open(fp, encoding="utf8") as f:
                for id_, row in enumerate(f):
                    yield f"{file_idx}_{id_}", {
                        "sentence": row.strip(),
                    }