# coding=utf-8
# Copyright 2023 The Inseq Team. All rights reserved.
#
# 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,
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"""SCAT: Supporting Context for Ambiguous Translations"""

import re
from pathlib import Path
from typing import Dict

import datasets
from datasets.utils.download_manager import DownloadManager


_CITATION = """\
@inproceedings{yin-etal-2021-context,
    title = "Do Context-Aware Translation Models Pay the Right Attention?",
    author = "Yin, Kayo  and
      Fernandes, Patrick  and
      Pruthi, Danish  and
      Chaudhary, Aditi  and
      Martins, Andr{\'e} F. T.  and
      Neubig, Graham",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.65",
    doi = "10.18653/v1/2021.acl-long.65",
    pages = "788--801",
}
"""

_DESCRIPTION = """\
The Supporting Context for Ambiguous Translations corpus (SCAT) is a dataset 
of English-to-French translations annotated with human rationales used for resolving ambiguity 
in pronoun anaphora resolution for multi-sentence translation.
"""

_URL = "https://huggingface.co/datasets/inseq/scat/raw/main/filtered_scat"

_HOMEPAGE = "https://github.com/neulab/contextual-mt/tree/master/data/scat"

_LICENSE = "Unknown"

class ScatConfig(datasets.BuilderConfig):
    def __init__(
        self,
        source_language: str, 
        target_language: str,
        **kwargs
    ):
        """BuilderConfig for MT-GenEval.
        Args:
            source_language: `str`, source language for translation.
            target_language: `str`, translation language.
            **kwargs: keyword arguments forwarded to super.
        """
        super().__init__(**kwargs)
        self.source_language = source_language
        self.target_language = target_language


class Scat(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [ScatConfig(name="sentences", source_language="en", target_language="fr")]

    DEFAULT_CONFIG_NAME = "sentences"

    @staticmethod
    def clean_string(txt: str):
        return txt.replace("<p>", "").replace("</p>", "").replace("<hon>", "").replace("<hoff>", "")

    @staticmethod
    def swap_pronoun(txt: str):
        pron: str = re.findall(r"<p>([^<]*)</p>", txt)[0]
        new_pron = pron
        is_cap = pron.istitle()
        if pron.lower() == "elles":
            new_pron = "ils"
        if pron.lower() == "elle":
            new_pron = "il"
        if pron.lower() == "ils":
            new_pron = "elles"
        if pron.lower() == "il":
            new_pron = "elle"
        if pron.lower() == "un":
            new_pron = "une"
        if pron.lower() == "une":
            new_pron = "un"
        if is_cap:
            new_pron = new_pron.capitalize()
        return txt.replace(f"<p>{pron}</p>", f"<p>{new_pron}</p>")

    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("int32"),
                "context_en": datasets.Value("string"),
                "en": datasets.Value("string"),
                "context_fr": datasets.Value("string"),
                "fr": datasets.Value("string"),
                "contrast_fr": datasets.Value("string"),
                "context_en_with_tags": datasets.Value("string"),
                "en_with_tags": datasets.Value("string"),
                "context_fr_with_tags": datasets.Value("string"),
                "fr_with_tags": datasets.Value("string"),
                "contrast_fr_with_tags": datasets.Value("string"),
                "has_supporting_context": datasets.Value("bool"),
                "has_supporting_preceding_context": datasets.Value("bool"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager: DownloadManager):
        """Returns SplitGenerators."""
        filepaths = {}
        splits = ["train", "valid", "test"]
        for split in splits:
            filepaths[split] = {}
            for lang in ["en", "fr"]:
                for ftype in ["context", ""]:
                    fname = f"filtered.{split}{'.' + ftype if ftype else ''}.{lang}"
                    name = f"{ftype}_{lang}" if ftype else lang
                    filepaths[split][name] = dl_manager.download_and_extract(f"{_URL}/{fname}")
        return [
            datasets.SplitGenerator(
                name=split_name,
                gen_kwargs={
                    "filepaths": filepaths[split],
                },
            )
            for split, split_name in zip(splits, ["train", "validation", "test"])
        ]


    def _generate_examples(
        self, filepaths: Dict[str, str]
    ):
        """ Yields examples as (key, example) tuples. """
        with open(filepaths["en"]) as f:
            en = f.read().splitlines()
        with open(filepaths["fr"]) as f:
            fr = f.read().splitlines()
        with open(filepaths["context_en"]) as f:
            context_en = f.read().splitlines()
        with open(filepaths["context_fr"]) as f:
            context_fr = f.read().splitlines()
        for i, (e, f, ce, cf) in enumerate(zip(en, fr, context_en, context_fr)):
            allfields = " ".join([e, f, ce, cf])
            has_supporting_context = False
            if "<hon>" in allfields and "<hoff>" in allfields:
                has_supporting_context = True
            contrast_fr = self.swap_pronoun(f)
            yield i, {
                "id": i,
                "context_en": self.clean_string(ce),
                "en": self.clean_string(e),
                "context_fr": self.clean_string(cf),
                "fr": self.clean_string(f),
                "contrast_fr": self.clean_string(contrast_fr),
                "context_en_with_tags": ce,
                "en_with_tags": e,
                "context_fr_with_tags": cf,
                "fr_with_tags": f,
                "contrast_fr_with_tags": contrast_fr,
                "has_supporting_context": has_supporting_context,
                "has_supporting_preceding_context": "<hon>" in cf,
            }