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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""ETPC: The Extended Typology Paraphrase Corpus"""

import os
from typing import Any, Dict, Generator, List, Optional, Tuple, Union

import datasets
from datasets.tasks import TextClassification
from lxml import etree

logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@inproceedings{kovatchev-etal-2018-etpc,
    title = "{ETPC} - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation",
    author = "Kovatchev, Venelin  and
      Mart{\'\i}, M. Ant{\`o}nia  and
      Salam{\'o}, Maria",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L18-1221",
}
"""

_DESCRIPTION = """\
The EPT typology addresses several practical limitations of existing paraphrase typologies: it is the first typology that copes with the non-paraphrase pairs in the paraphrase identification corpora and distinguishes between contextual and habitual paraphrase types. ETPC is the largest corpus to date annotated with atomic paraphrase types.
"""

_HOMEPAGE = "https://github.com/venelink/ETPC"

_LICENSE = "Unknown"

_URLS = [
    "https://raw.githubusercontent.com/venelink/ETPC/master/Corpus/text_pairs.xml",
    "https://raw.githubusercontent.com/venelink/ETPC/master/Corpus/textual_paraphrases.xml",
]


class ETPC(datasets.GeneratorBasedBuilder):
    """ETPC dataset."""

    VERSION = datasets.Version("0.95.0")

    def _info(self):
        features = datasets.Features(
            {
                "idx": datasets.Value("string"),
                "sentence1": datasets.Value("string"),
                "sentence2": datasets.Value("string"),
                "sentence1_tokenized": datasets.Sequence(
                    datasets.Value("string")
                ),
                "sentence2_tokenized": datasets.Sequence(
                    datasets.Value("string")
                ),
                "etpc_label": datasets.Value("int8"),
                "mrpc_label": datasets.Value("int8"),
                "negation": datasets.Value("int8"),
                "paraphrase_types": datasets.Sequence(
                    datasets.Value("string")
                ),
                "paraphrase_type_ids": datasets.Sequence(
                    datasets.Value("string")
                ),
                "sentence1_segment_location": datasets.Sequence(
                    datasets.Value("int32")
                ),
                "sentence2_segment_location": datasets.Sequence(
                    datasets.Value("int32")
                ),
                "sentence1_segment_text": datasets.Value("string"),
                "sentence2_segment_text": datasets.Value("string"),
            }
        )

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

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

    def _generate_examples(self, file_paths):

        file_paths = list(file_paths)
        text_pairs_path = file_paths[0]
        paraphrase_types_path = file_paths[1]

        parser = etree.XMLParser(encoding="utf-8", recover=True)

        tree_text_pairs = etree.parse(text_pairs_path, parser=parser)
        tree_paraphrase_types = etree.parse(
            paraphrase_types_path, parser=parser
        )

        root_text_pairs = tree_text_pairs.getroot()
        root_paraphrase_types = tree_paraphrase_types.getroot()

        idx = 0

        for row in root_text_pairs:
            current_pair_id = row.find(".//pair_id").text
            paraphrase_types = root_paraphrase_types.xpath(
                f".//pair_id[text()='{current_pair_id}']/parent::relation/type_name/text()"
            )
            paraphrase_type_ids = root_paraphrase_types.xpath(
                f".//pair_id[text()='{current_pair_id}']/parent::relation/type_id/text()"
            )
            sentence1_segment_location = root_paraphrase_types.xpath(
                f".//pair_id[text()='{current_pair_id}']/parent::relation/s1_scope/text()"
            )
            sentence2_segment_location = root_paraphrase_types.xpath(
                f".//pair_id[text()='{current_pair_id}']/parent::relation/s2_scope/text()"
            )
            sentence1_segment_text = root_paraphrase_types.xpath(
                f".//pair_id[text()='{current_pair_id}']/parent::relation/s1_text/text()"
            )
            sentence2_segment_text = root_paraphrase_types.xpath(
                f".//pair_id[text()='{current_pair_id}']/parent::relation/s2_text/text()"
            )
            sentence1_tokenized = row.find(".//sent1_tokenized").text.split()
            sentence2_tokenized = row.find(".//sent2_tokenized").text.split()

            sentence1_segment_location_full = [0] * len(sentence1_tokenized)
            sentence2_segment_location_full = [0] * len(sentence2_tokenized)

            for (
                sentence1_segment_location_example,
                sentence2_segment_location_example,
                paraphrase_type_id,
            ) in zip(
                sentence1_segment_location,
                sentence2_segment_location,
                paraphrase_type_ids,
            ):
                for (
                    segment_location
                ) in sentence1_segment_location_example.split(","):
                    sentence1_segment_location_full[
                        int(segment_location)
                    ] = int(paraphrase_type_id)
                for (
                    segment_location
                ) in sentence2_segment_location_example.split(","):
                    sentence2_segment_location_full[
                        int(segment_location)
                    ] = int(paraphrase_type_id)

            yield idx, {
                "idx": row.find(".//pair_id").text + "_" + str(idx),
                "sentence1": row.find(".//sent1_raw").text,
                "sentence2": row.find(".//sent2_raw").text,
                "sentence1_tokenized": sentence1_tokenized,
                "sentence2_tokenized": sentence2_tokenized,
                "etpc_label": int(row.find(".//etpc_label").text),
                "mrpc_label": int(row.find(".//mrpc_label").text),
                "negation": int(row.find(".//negation").text),
                "paraphrase_types": paraphrase_types,
                "paraphrase_type_ids": paraphrase_type_ids,
                "sentence1_segment_location": sentence1_segment_location_full,
                "sentence2_segment_location": sentence2_segment_location_full,
                "sentence1_segment_text": sentence1_segment_text,
                "sentence2_segment_text": sentence2_segment_text,
            }

            idx += 1