import json

import datasets
import pandas as pd

SUPPORTED_LANGUAGES = [
    "sl",
    "ur",
    "sw",
    "uz",
    "vi",
    "sq",
    "ms",
    "km",
    "hy",
    "da",
    "ky",
    "mg",
    "mn",
    "ja",
    "el",
    "it",
    "is",
    "ru",
    "tl",
    "so",
    "pt",
    "uk",
    "sr",
    "sn",
    "ht",
    "bs",
    "my",
    "ar",
    "hr",
    "nl",
    "bn",
    "ne",
    "hi",
    "ka",
    "az",
    "ko",
    "id",
    "fr",
    "es",
    "en",
    "fa",
    "lo",
    "iw",
    "th",
    "tr",
    "zht",
    "zhs",
    "ti",
    "tg",
    "control",
]
SUPPORTED_SOURCES = ["llm"]

ROOT_DIR = "data"


class LangConfig(datasets.BuilderConfig):
    def __init__(self, language, source="all", n_hits=10, **kwargs):
        super(LangConfig, self).__init__(**kwargs)
        # TODO: support specifying multiple languages
        if language == "all":
            languages = SUPPORTED_LANGUAGES
        else:
            languages = [language]
        self.languages = languages
        self.source = source
        self.n_hits = n_hits
        self.data_root_dir = ROOT_DIR


class BordIRLinesDataset(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        LangConfig(name=lang, language=lang, description=f"{lang.upper()} dataset")
        for lang in SUPPORTED_LANGUAGES
    ] + [LangConfig(name="all", language="all", description="All languages dataset")]

    def _info(self):
        return datasets.DatasetInfo(
            description="IR Dataset for BordIRLines paper.",
            features=datasets.Features(
                {
                    "query_id": datasets.Value("string"),
                    "query": datasets.Value("string"),
                    "territory": datasets.Value("string"),
                    "rank": datasets.Value("int32"),
                    "score": datasets.Value("float32"),
                    "doc_id": datasets.Value("string"),
                    "doc_text": datasets.Value("string"),
                }
            ),
        )

    def _split_generators(self, dl_manager):
        base_url = self.config.data_root_dir
        downloaded_queries = dl_manager.download_and_extract(
            {
                "queries": f"{base_url}/queries.tsv",
            }
        )

        languages_to_download = self.config.languages
        sources_to_download = (
            SUPPORTED_SOURCES if self.config.source == "all" else [self.config.source]
        )

        splits = []
        for lang in languages_to_download:
            downloaded_data = {}
            for source in sources_to_download:
                lang_docs = lang if lang != "control" else "en"
                downloaded_data[source] = dl_manager.download_and_extract(
                    {
                        "docs": f"{base_url}/{lang_docs}/{source}/{lang_docs}_docs.json",
                        "hits": f"{base_url}/{lang}/{source}/{lang}_query_hits.tsv",
                    }
                )

            split = datasets.SplitGenerator(
                name=lang,
                gen_kwargs={
                    "language": lang,
                    "downloaded_data": downloaded_data,
                    "queries_path": downloaded_queries["queries"],
                },
            )
            splits.append(split)
        return splits

    def _generate_examples(self, language, downloaded_data, queries_path):
        source = self.config.source
        n_hits = self.config.n_hits

        if language not in SUPPORTED_LANGUAGES:
            raise ValueError(
                f"Language {language} is not supported. Supported languages: {SUPPORTED_LANGUAGES}"
            )
        if source not in SUPPORTED_SOURCES and source != "all":
            raise ValueError(
                f"Source {source} is not supported. Supported sources: {SUPPORTED_SOURCES}"
            )
        queries_df = pd.read_csv(queries_path, sep="\t")
        query_map = dict(zip(queries_df["query_id"], queries_df["query_text"]))
        counter = 0
        for src in downloaded_data:
            if source != "all" and src != source:
                continue
            docs_path = downloaded_data[src]["docs"]
            hits_path = downloaded_data[src]["hits"]

            with open(docs_path, "r", encoding="utf-8") as f:
                docs = json.load(f)

            hits = pd.read_csv(hits_path, sep="\t")
            if n_hits:
                hits = hits.groupby("query_id").head(n_hits)
            for _, row in hits.iterrows():
                doc_id = row["doc_id"]
                if doc_id in docs:
                    query_id = row["query_id"]
                    query_text = query_map.get(query_id, "")
                    yield (
                        counter,
                        {
                            "query_id": query_id,
                            "query": query_text,
                            "territory": row["territory"],
                            "rank": row["rank"],
                            "score": row["score"],
                            "doc_id": doc_id,
                            "doc_text": docs[doc_id],
                        },
                    )

                    counter += 1