"""Pure data contract for RAGAS evaluation of frozen ADC answers."""
from __future__ import annotations

import math
from collections import defaultdict

METRICS = ("faithfulness", "answer_relevancy", "context_recall", "answer_correctness")


def sample_from_answer(row, case):
    contexts = []
    for ref in row.get("response", {}).get("references") or []:
        content = ref.get("content") or []
        if isinstance(content, str):
            raise ValueError("Expected a list of source chunks, not a single string")
        for text in content:
            if not isinstance(text, str):
                raise ValueError("Source chunks must be strings")
            if text.strip() and text not in contexts:
                contexts.append(text)
    return {
        "user_input": case["question"],
        "response": row.get("response", {}).get("response", ""),
        "reference": case["reference_answer"],
        "retrieved_contexts": contexts,
    }


def skip_reason(row, case, metric, sample):
    if row["status"] != "ok":
        return "generation_failed"
    if case["expected"] == "abstain_or_request_details":
        return "abstention_scenario_evaluated_separately"
    if metric in {"faithfulness", "context_recall"}:
        if row["mode"] == "bypass":
            return "no_retrieval_in_baseline"
        if not sample["retrieved_contexts"]:
            return "missing_source_contexts"
    return None


def checked_score(metric, value):
    value = float(value)
    if not math.isfinite(value):
        raise ValueError("RAGAS returned a nonfinite score")
    minimum = -1 if metric == "answer_relevancy" else 0
    if not minimum <= value <= 1:
        raise ValueError("RAGAS score outside its mathematical range")
    return value


def aggregate(rows):
    # Each pair/metric contributes at most one latest attempt.
    latest = {(r["case_id"], r["mode"], r["metric"]): r for r in rows}
    groups = defaultdict(list)
    for row in latest.values():
        groups[(row["mode"], row["metric"])].append(row)
    summary = {}
    for (mode, metric), values in groups.items():
        good = [checked_score(metric, r["value"]) for r in values if r["status"] == "ok"]
        summary.setdefault(mode, {})[metric] = {
            "mean": sum(good) / len(good) if good else None,
            "n_ok": len(good),
            "n_error": sum(r["status"] == "error" for r in values),
            "n_undefined": sum(r["status"] == "undefined" for r in values),
            "n_skipped": sum(r["status"] == "skipped" for r in values),
        }
    return summary