from __future__ import annotations

from typing import Any, TYPE_CHECKING

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

if TYPE_CHECKING:
    from lm_eval.api.task import ConfigurableTask


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

task_names = ("claim_stance_topic",)

class ClaimExtractor(LMEvalBenchmarkExtractor):
    """Extractor for Claim benchmark."""


    evaluator_name = "log_likelihoods"
    def extract_contrastive_pairs(
        self,
        lm_eval_task_data: ConfigurableTask,
        limit: int | None = None,
        preferred_doc: str | None = None,
    ) -> list[ContrastivePair]:
        log = bind(_LOG, task=getattr(lm_eval_task_data, "NAME", "unknown"))
        max_items = self._normalize_limit(limit)
        docs = self.load_docs(lm_eval_task_data, max_items, preferred_doc=preferred_doc)
        pairs: list[ContrastivePair] = []
        log.info("Extracting contrastive pairs", extra={"doc_count": len(docs)})

        for doc in docs:
            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:
            task_name = getattr(lm_eval_task_data, "NAME", type(lm_eval_task_data).__name__)
            log.warning("No valid pairs extracted", extra={"task": task_name})

        return pairs

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

        try:
            # Format 1: Unitxt format (claim_stance_topic) with task_data JSON
            if "task_data" in doc and "target" in doc:
                import json
                task_data_str = doc.get("task_data", "")
                if task_data_str:
                    task_data = json.loads(task_data_str)
                    text = task_data.get("text", "").strip()
                    classes = task_data.get("classes", [])
                    correct_label = doc.get("target", "").strip()

                    if not text or not classes or not correct_label:
                        log.debug("Skipping unitxt doc - missing fields", extra={"doc": doc})
                        return None

                    # Get incorrect label (pick first one that's not the correct one)
                    incorrect_label = None
                    for cls in classes:
                        if cls != correct_label:
                            incorrect_label = cls
                            break

                    if not incorrect_label:
                        log.debug("Skipping unitxt doc - no incorrect label found", extra={"doc": doc})
                        return None

                    # Format the prompt from source if available, otherwise construct it
                    if "source" in doc:
                        prompt = str(doc.get("source", "")).strip()
                    else:
                        text_type = task_data.get("text_type", "Argument")
                        prompt = f"{text_type}: {text}\nTopic:"

                    metadata = {"label": "claim"}
                    return self._build_pair(
                        question=prompt,
                        correct=correct_label,
                        incorrect=incorrect_label,
                        metadata=metadata,
                    )

            # Format 2: Standard multiple choice format
            # Try multiple format patterns for question
            question = doc.get("question", doc.get("query", doc.get("input", doc.get("instruction", doc.get("prompt", ""))))).strip()

            # Try multiple format patterns for choices
            choices = doc.get("choices", doc.get("options", doc.get("answers", [])))

            # Handle option_a/b/c/d format
            if not choices and "option_a" in doc:
                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]

            # Try multiple format patterns for answer
            answer = doc.get("answer", doc.get("label", doc.get("target", None)))

            if isinstance(answer, str) and len(answer) == 1 and answer.isalpha():
                answer_idx = ord(answer.upper()) - ord('A')
            elif isinstance(answer, int):
                answer_idx = answer
            else:
                return None

            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 = str(choices[answer_idx]).strip()
            incorrect_idx = (answer_idx + 1) % len(choices)
            incorrect = str(choices[incorrect_idx]).strip()
            metadata = {"label": "claim"}

            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

    @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"))
