from __future__ import annotations

from typing import Any

from wisent.core.contrastive_pairs.core.pair import ContrastivePair
from wisent.core.contrastive_pairs.core.response import NegativeResponse, PositiveResponse
from wisent.core.contrastive_pairs.huggingface_pairs.atoms import HuggingFaceBenchmarkExtractor
from wisent.core.cli_logger import setup_logger, bind


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

task_names = ("global_mmlu_ar",)

evaluator_name = "log_likelihoods"


class GlobalMmluArExtractor(HuggingFaceBenchmarkExtractor):
    """Extractor for Global MMLU Arabic - multiple choice questions."""

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

        from datasets import load_dataset
        try:
            dataset = load_dataset("CohereForAI/Global-MMLU", "ar", split="test")
            if max_items:
                dataset = dataset.select(range(min(max_items, len(dataset))))
        except Exception as e:
            log.error(f"Failed to load global_mmlu_ar 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": "global_mmlu_ar"})

        return pairs

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

        try:
            question = str(doc.get("instruction", doc.get("question", ""))).strip()

            # Global MMLU format uses option_a/b/c/d
            choices = [
                str(doc.get("option_a", "")).strip(),
                str(doc.get("option_b", "")).strip(),
                str(doc.get("option_c", "")).strip(),
                str(doc.get("option_d", "")).strip(),
            ]
            choices = [c for c in choices if c]

            answer = doc.get("answer", "A")
            answer_idx = ord(str(answer).upper()) - ord('A')

            if not question or not choices or not (0 <= answer_idx < len(choices)):
                log.debug("Skipping doc due to missing/invalid fields", extra={"doc": doc})
                return None

            correct = choices[answer_idx]
            incorrect_idx = (answer_idx + 1) % len(choices)
            incorrect = choices[incorrect_idx]

            formatted_question = f"Question: {question}\nA. {incorrect}\nB. {correct}"
            metadata = {"label": "global_mmlu_ar"}

            return self._build_pair(
                question=formatted_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

    @staticmethod
    def _build_pair(
        question: str,
        correct: str,
        incorrect: str,
        metadata: dict[str, Any] | None = None,
    ) -> ContrastivePair:
        positive_response = PositiveResponse(model_response=correct)
        negative_response = NegativeResponse(model_response=incorrect)
        return ContrastivePair(prompt=question, positive_response=positive_response, negative_response=negative_response, label=metadata.get("label"))
