import csv
import datasets

_CITATION = """\
@misc{abdallah2025good,
    title={How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models},
    author={Abdelrahman Abdallah and Bhawna Piryani and Jamshid Mozafari and Mohammed Ali and Adam Jatowt},
    year={2025},
    eprint={2508.16757},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
"""

_DESCRIPTION = """\
FutureQueryEval is a novel IR benchmark comprising 148 queries with 2,938 query-document pairs 
across 7 topical categories, designed to evaluate reranker performance on temporal novelty.
All queries refer to events after April 2025 to ensure zero contamination with LLM pretraining data.
"""

_HOMEPAGE = "https://github.com/DataScienceUIBK/llm-reranking-generalization-study"

_LICENSE = "Apache-2.0"

_URLS = {
    "queries": "queries.csv",
    "corpus": "corpus.tsv", 
    "qrels": "qrels.txt",
}

class FutureQueryEval(datasets.GeneratorBasedBuilder):
    """FutureQueryEval dataset for temporal IR evaluation."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="queries",
            version=VERSION,
            description="Query collection with categories",
        ),
        datasets.BuilderConfig(
            name="corpus", 
            version=VERSION,
            description="Document corpus",
        ),
        datasets.BuilderConfig(
            name="qrels",
            version=VERSION, 
            description="Relevance judgments",
        ),
    ]

    DEFAULT_CONFIG_NAME = "queries"

    def _info(self):
        if self.config.name == "queries":
            features = datasets.Features({
                "query_id": datasets.Value("string"),
                "query_text": datasets.Value("string"), 
                "category": datasets.Value("string"),
            })
        elif self.config.name == "corpus":
            features = datasets.Features({
                "doc_id": datasets.Value("string"),
                "title": datasets.Value("string"),
                "text": datasets.Value("string"),
                "url": datasets.Value("string"),
            })
        elif self.config.name == "qrels":
            features = datasets.Features({
                "query_id": datasets.Value("string"),
                "iteration": datasets.Value("int32"),
                "doc_id": datasets.Value("string"), 
                "relevance": datasets.Value("int32"),
            })

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

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download(_URLS)
        
        if self.config.name == "queries":
            return [
                datasets.SplitGenerator(
                    name="queries",
                    gen_kwargs={"filepath": downloaded_files["queries"]},
                ),
            ]
        elif self.config.name == "corpus":
            return [
                datasets.SplitGenerator(
                    name="corpus", 
                    gen_kwargs={"filepath": downloaded_files["corpus"]},
                ),
            ]
        elif self.config.name == "qrels":
            return [
                datasets.SplitGenerator(
                    name="qrels",
                    gen_kwargs={"filepath": downloaded_files["qrels"]},
                ),
            ]

    def _generate_examples(self, filepath):
        if self.config.name == "queries":
            with open(filepath, encoding="utf-8") as f:
                reader = csv.DictReader(f, delimiter=",")
                for key, row in enumerate(reader):
                    yield key, {
                        "query_id": row["query_id"],
                        "query_text": row["query_text"],
                        "category": row["category"],
                    }
                    
        elif self.config.name == "corpus":
            with open(filepath, encoding="utf-8") as f:
                reader = csv.DictReader(f, delimiter="\t")
                for key, row in enumerate(reader):
                    yield key, {
                        "doc_id": row["doc_id"],
                        "title": row["title"], 
                        "text": row["text"],
                        "url": row["url"],
                    }
                    
        elif self.config.name == "qrels":
            with open(filepath, encoding="utf-8") as f:
                for key, line in enumerate(f):
                    parts = line.strip().split()
                    if len(parts) == 4:
                        yield key, {
                            "query_id": parts[0],
                            "iteration": int(parts[1]),
                            "doc_id": parts[2], 
                            "relevance": int(parts[3]),
                        }