from __future__ import annotations

from typing import Any

from wisent.core.contrastive_pairs.pair import ContrastivePair
from wisent.extractors.hf._registry.manifest.atoms import HuggingFaceBenchmarkExtractor
from wisent.core.cli.cli_logger import setup_logger, bind


__all__ = ["BhtcV2Extractor"]
_LOG = setup_logger(__name__)

task_names = ("bhtc_v2",)

class BhtcV2Extractor(HuggingFaceBenchmarkExtractor):
    """Extractor for BHTC v2 - Basque text classification."""


    evaluator_name = "log_likelihoods"
    def extract_contrastive_pairs(
        self,
        limit: int | None = None,
    ) -> list[ContrastivePair]:
        log = bind(_LOG, task="bhtc_v2")
        max_items = self._normalize_limit(limit)

        # Load dataset from HuggingFace
        from datasets import load_dataset
        try:
            dataset = load_dataset("orai-nlp/basqueGLUE", "bhtc", split="test")
            if max_items:
                dataset = dataset.select(range(min(max_items, len(dataset))))
        except Exception as e:
            log.error(f"Failed to load bhtc_v2 dataset: {e}")
            return []

        pairs: list[ContrastivePair] = []
        log.info("Extracting contrastive pairs", extra={"doc_count": len(dataset)})

        for doc in dataset:
            pair = self._extract_pair_from_doc(doc)
            if pair is not None:
                pairs.append(pair)
                if max_items is not None and len(pairs) >= max_items:
                    break

        if not pairs:
            log.warning("No valid pairs extracted", extra={"task": "bhtc_v2"})

        return pairs

    def _extract_pair_from_doc(self, doc: dict[str, Any]) -> ContrastivePair | None:
        log = bind(_LOG, doc_id=doc.get("id", "unknown"))

        try:
            text = doc.get("text", "").strip()
            label = doc.get("label")

            if not text or label is None:
                log.debug("Skipping doc due to missing text or label", extra={"doc": doc})
                return None

            # BHTC categories (Basque)
            categories = ['Ekonomia', 'Euskal Herria', 'Euskara', 'Gizartea', 'Historia', 'Ingurumena', 'Iritzia', 'Komunikazioa', 'Kultura', 'Nazioartea', 'Politika', 'Zientzia']

            if not isinstance(label, int) or not (0 <= label < len(categories)):
                log.debug(f"Invalid label: {label}", extra={"doc": doc})
                return None

            correct = categories[label]
            incorrect_idx = (label + 1) % len(categories)
            incorrect = categories[incorrect_idx]

            question = f"Text: {text}\nQuestion: What is the topic of the above text?\nA. {incorrect}\nB. {correct}"
            metadata = {"label": "bhtc_v2"}

            return self._build_pair(
                question=question,
                correct=correct,
                incorrect=incorrect,
                metadata=metadata,
            )

        except Exception as exc:
            log.error("Error extracting pair from doc", exc_info=exc, extra={"doc": doc})
            return None

