# 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.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Carolina Corpus"""

from lxml import etree
import os
import datasets


_HOMEPAGE = "https://sites.usp.br/corpuscarolina/"


_DESCRIPTION = """
Carolina is an Open Corpus for Linguistics and Artificial Intelligence with a
robust volume of texts of varied typology in contemporary Brazilian Portuguese
(1970-2021).
"""


_CITATION = r"""
@misc{corpusCarolinaV1.1,
    title={
        Carolina:
        The Open Corpus for Linguistics and Artificial Intelligence},
    author={
        Finger, Marcelo and
        Paixão de Sousa, Maria Clara and
        Namiuti, Cristiane and
        Martins do Monte, Vanessa and
        Costa, Aline Silva and
        Serras, Felipe Ribas and
        Sturzeneker, Mariana Lourenço and
        Guets, Raquel de Paula and
        Mesquita, Renata Morais and
        Mello, Guilherme Lamartine de and
        Crespo, Maria Clara Ramos Morales and
        Rocha, Maria Lina de Souza Jeannine and
        Brasil, Patrícia and
        Silva, Mariana Marques da and
        Palma, Mayara Feliciano},
    howpublished={\url{https://sites.usp.br/corpuscarolina/corpus}},
    year={2022},
    note={Version 1.1 (Ada)},
}
"""


_LICENSE = """
The Open Corpus for Linguistics and Artificial Intelligence (Carolina) was
compiled for academic purposes, namely linguistic and computational analysis.
It is composed of texts assembled in various digital repositories, whose
licenses are multiple and therefore should be observed when making use of the
corpus. The Carolina headers are licensed under Creative Commons
Attribution-NonCommercial-ShareAlike 4.0 International."
"""


def _taxonomies():
    """Creates a map between taxonomy code and name

    Returns
    -------
    dict
        The dictionary of codes and names.
    """
    return dict(
        dat="datasets and other corpora",
        jud="judicial branch",
        leg="legislative branch",
        pub="public domain works",
        soc="social media",
        uni="university_domains",
        wik="wikis",
    )


_VERSION = "1.1.0"
_CORPUS_URL = "corpus/{taxonomy}/"
_CHECKSUM_FNAME = _CORPUS_URL + "checksum.sha256"


class CarolinaConfig(datasets.BuilderConfig):
    """Carolina Configuration."""
    def __init__(self, taxonomy: str = None, **kwargs):
        """BuilderConfig for Carolina

        Parameters
        ----------
        taxonomy : str
            The taxonomy code (3 letters). The code defines the taxonomy
            to download. If `None`, all taxonomies will be downloaded.
        **kwargs
            Arguments passed to super.
        """
        # validates taxonomy
        if taxonomy is None:
            taxonomy = "all"
        elif taxonomy != "all" and taxonomy not in _taxonomies():
            raise ValueError(f"Invalid taxonomy: {taxonomy}")

        # custom name and description
        description = "Carolina corpus."
        if taxonomy == "all":
            name = "carolina"
            description += " Using all taxonomies."
        else:
            name = _taxonomies()[taxonomy]
            description += f" Using taxonomy {taxonomy}"

        super(CarolinaConfig, self).__init__(
            name=name, description=description, **kwargs)

        # Carolina attributes
        self.taxonomy = taxonomy
        self.version = datasets.Version(_VERSION)


class Carolina(datasets.GeneratorBasedBuilder):
    """Carolina Downloader and Builder"""

    BUILDER_CONFIG_CLASS = CarolinaConfig

    def _info(self):
        features = datasets.Features({
            "meta": datasets.Value("string"),
            "text": datasets.Value("string")
        })

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

    def _split_generators(self, dl_manager):
        # list taxonomies to download
        if self.config.taxonomy == "all":
            taxonomies = _taxonomies().values()
        else:
            taxonomies = [_taxonomies()[self.config.taxonomy]]

        zip_urls = dict()
        for taxonomy in taxonomies:
            # download checksum file
            checksum_path = _CHECKSUM_FNAME.format(taxonomy=taxonomy)
            checksum_path = dl_manager.download(checksum_path)

            tax_url = _CORPUS_URL.format(taxonomy=taxonomy)

            # extract and build zip urls
            with open(checksum_path, encoding="utf-8") as cfile:
                for line in cfile:
                    fname = line.split()[1]
                    if fname.endswith(".xml.zip"):
                        zip_url = tax_url + fname           # download url
                        fname = os.path.split(fname)[1]     # removes subdirs
                        fname = fname[:-4]                  # removes .zip
                        zip_urls[fname] = zip_url           # xml -> zip url

        # extractions are made in cache folders and
        # the path returned is the folder path, not the
        # extracted file (or files). It is necessary to
        # build the xml file path. It is made using the
        # zip_urls dict structure.
        extracted = dl_manager.download_and_extract(zip_urls)
        xml_files = [os.path.join(v, k) for k, v in extracted.items()]
        xml_files = sorted(xml_files)

        return [
            datasets.SplitGenerator(
                name="corpus",
                gen_kwargs={"filepaths": xml_files}
            )
        ]

    def _generate_examples(self, filepaths):
        TEI_NS = "{http://www.tei-c.org/ns/1.0}"
        parser_params = dict(
            huge_tree=True,
            encoding="utf-8",
            tag=f"{TEI_NS}TEI"
        )

        _key = 0
        for path in filepaths:
            # parse xml file
            for _, tei in etree.iterparse(path, **parser_params):
                header = tei.find(f"{TEI_NS}teiHeader")

                example = {
                    "meta": etree.tostring(
                        header, encoding="utf-8").decode("utf-8"),
                    "text": tei.find(f".//{TEI_NS}body/{TEI_NS}p").text
                }
                yield _key, example
                _key += 1