"""
Insurance AI Reliability Benchmark - Evaluation Script

Scores AI agent responses against a ground-truth benchmark for
insurance domain tasks: intent classification, routing decisions,
and action completeness.

Usage as CLI:
    python evaluate.py --benchmark data/test.jsonl --predictions predictions.jsonl --output results.json

Usage as library:
    from evaluate import BenchmarkEvaluator
    evaluator = BenchmarkEvaluator.from_jsonl("data/test.jsonl")
    results = evaluator.evaluate(predictions)
"""

from __future__ import annotations

import argparse
import json
import sys
from collections import defaultdict
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any


# --- Data Models ---


@dataclass
class BenchmarkItem:
    """A single ground-truth benchmark entry."""

    id: str
    expected_intent: str
    expected_routing: str
    expected_actions: list[str]
    category: str = "uncategorized"
    difficulty: str = "medium"

    @classmethod
    def from_dict(cls, data: dict[str, Any]) -> BenchmarkItem:
        """Parse a benchmark item from a dictionary."""
        return cls(
            id=data["id"],
            expected_intent=data["expected_intent"],
            expected_routing=data["expected_routing"],
            expected_actions=data["expected_actions"],
            category=data.get("category", "uncategorized"),
            difficulty=data.get("difficulty", "medium"),
        )


@dataclass
class Prediction:
    """A single prediction from the AI agent."""

    id: str
    predicted_intent: str
    predicted_routing: str
    predicted_actions: list[str]

    @classmethod
    def from_dict(cls, data: dict[str, Any]) -> Prediction:
        """Parse a prediction from a dictionary."""
        return cls(
            id=data["id"],
            predicted_intent=data["predicted_intent"],
            predicted_routing=data["predicted_routing"],
            predicted_actions=data["predicted_actions"],
        )


@dataclass
class ItemScore:
    """Scores for a single benchmark item."""

    id: str
    intent_correct: bool
    routing_correct: bool
    action_completeness: float
    category: str
    difficulty: str


@dataclass
class GroupMetrics:
    """Aggregated metrics for a group of items (category, difficulty, or overall)."""

    count: int = 0
    intent_accuracy: float = 0.0
    routing_accuracy: float = 0.0
    mean_action_completeness: float = 0.0
    composite_score: float = 0.0


@dataclass
class EvaluationResults:
    """Full evaluation results across all items."""

    overall: GroupMetrics = field(default_factory=GroupMetrics)
    by_category: dict[str, GroupMetrics] = field(default_factory=dict)
    by_difficulty: dict[str, GroupMetrics] = field(default_factory=dict)
    item_scores: list[ItemScore] = field(default_factory=list)
    missing_predictions: list[str] = field(default_factory=list)
    extra_predictions: list[str] = field(default_factory=list)

    def to_dict(self) -> dict[str, Any]:
        """Serialize results to a dictionary."""
        return {
            "overall": _group_to_dict(self.overall),
            "by_category": {k: _group_to_dict(v) for k, v in self.by_category.items()},
            "by_difficulty": {k: _group_to_dict(v) for k, v in self.by_difficulty.items()},
            "item_scores": [
                {
                    "id": s.id,
                    "intent_correct": s.intent_correct,
                    "routing_correct": s.routing_correct,
                    "action_completeness": round(s.action_completeness, 4),
                    "category": s.category,
                    "difficulty": s.difficulty,
                }
                for s in self.item_scores
            ],
            "missing_predictions": self.missing_predictions,
            "extra_predictions": self.extra_predictions,
        }

    def summary(self) -> str:
        """Return a human-readable summary."""
        lines = [
            "=" * 60,
            "Insurance AI Reliability Benchmark - Results",
            "=" * 60,
            "",
            f"  Items evaluated:    {self.overall.count}",
            f"  Missing predictions: {len(self.missing_predictions)}",
            f"  Extra predictions:   {len(self.extra_predictions)}",
            "",
            "Overall Metrics:",
            f"  Intent Accuracy:       {self.overall.intent_accuracy:.2%}",
            f"  Routing Accuracy:      {self.overall.routing_accuracy:.2%}",
            f"  Action Completeness:   {self.overall.mean_action_completeness:.2%}",
            f"  Composite Score:       {self.overall.composite_score:.2%}",
            "",
        ]

        if self.by_category:
            lines.append("By Category:")
            for name, m in sorted(self.by_category.items()):
                lines.append(
                    f"  {name:30s}  n={m.count:3d}  "
                    f"intent={m.intent_accuracy:.2%}  "
                    f"routing={m.routing_accuracy:.2%}  "
                    f"actions={m.mean_action_completeness:.2%}  "
                    f"composite={m.composite_score:.2%}"
                )
            lines.append("")

        if self.by_difficulty:
            lines.append("By Difficulty:")
            for name, m in sorted(self.by_difficulty.items()):
                lines.append(
                    f"  {name:30s}  n={m.count:3d}  "
                    f"intent={m.intent_accuracy:.2%}  "
                    f"routing={m.routing_accuracy:.2%}  "
                    f"actions={m.mean_action_completeness:.2%}  "
                    f"composite={m.composite_score:.2%}"
                )
            lines.append("")

        lines.append("=" * 60)
        return "\n".join(lines)


def _group_to_dict(g: GroupMetrics) -> dict[str, Any]:
    """Serialize a GroupMetrics to a dictionary."""
    return {
        "count": g.count,
        "intent_accuracy": round(g.intent_accuracy, 4),
        "routing_accuracy": round(g.routing_accuracy, 4),
        "mean_action_completeness": round(g.mean_action_completeness, 4),
        "composite_score": round(g.composite_score, 4),
    }


# --- Core Metrics ---

# Weights for the composite reliability score.
INTENT_WEIGHT = 0.3
ROUTING_WEIGHT = 0.4
ACTIONS_WEIGHT = 0.3


def jaccard_similarity(a: list[str], b: list[str]) -> float:
    """Jaccard similarity between two lists treated as sets.

    Returns 1.0 if both are empty (nothing expected, nothing predicted).
    """
    set_a = set(a)
    set_b = set(b)
    if not set_a and not set_b:
        return 1.0
    union = set_a | set_b
    if not union:
        return 1.0
    return len(set_a & set_b) / len(union)


def composite_score(
    intent_acc: float,
    routing_acc: float,
    action_comp: float,
) -> float:
    """Weighted composite reliability score."""
    return (
        INTENT_WEIGHT * intent_acc
        + ROUTING_WEIGHT * routing_acc
        + ACTIONS_WEIGHT * action_comp
    )


def _aggregate(scores: list[ItemScore]) -> GroupMetrics:
    """Aggregate a list of item scores into group metrics."""
    if not scores:
        return GroupMetrics()

    n = len(scores)
    intent_acc = sum(s.intent_correct for s in scores) / n
    routing_acc = sum(s.routing_correct for s in scores) / n
    action_comp = sum(s.action_completeness for s in scores) / n

    return GroupMetrics(
        count=n,
        intent_accuracy=intent_acc,
        routing_accuracy=routing_acc,
        mean_action_completeness=action_comp,
        composite_score=composite_score(intent_acc, routing_acc, action_comp),
    )


# --- Evaluator ---


class BenchmarkEvaluator:
    """Evaluates AI agent predictions against a ground-truth benchmark.

    Args:
        benchmark: List of ground-truth benchmark items.
    """

    def __init__(self, benchmark: list[BenchmarkItem]) -> None:
        self.benchmark = {item.id: item for item in benchmark}
        if len(self.benchmark) != len(benchmark):
            dupes = len(benchmark) - len(self.benchmark)
            raise ValueError(f"Benchmark contains {dupes} duplicate ID(s).")

    @classmethod
    def from_jsonl(cls, path: str | Path) -> BenchmarkEvaluator:
        """Load benchmark from a JSONL file.

        Each line must be a JSON object with at least:
            id, expected_intent, expected_routing, expected_actions

        Optional fields: category, difficulty
        """
        items: list[BenchmarkItem] = []
        path = Path(path)
        with path.open("r", encoding="utf-8") as f:
            for lineno, line in enumerate(f, start=1):
                line = line.strip()
                if not line:
                    continue
                try:
                    data = json.loads(line)
                except json.JSONDecodeError as e:
                    raise ValueError(f"Invalid JSON at {path}:{lineno}: {e}") from e
                try:
                    items.append(BenchmarkItem.from_dict(data))
                except KeyError as e:
                    raise ValueError(f"Missing field {e} at {path}:{lineno}") from e
        if not items:
            raise ValueError(f"No benchmark items found in {path}")
        return cls(items)

    def score_item(self, item: BenchmarkItem, pred: Prediction) -> ItemScore:
        """Score a single prediction against its benchmark item."""
        return ItemScore(
            id=item.id,
            intent_correct=pred.predicted_intent == item.expected_intent,
            routing_correct=pred.predicted_routing == item.expected_routing,
            action_completeness=jaccard_similarity(
                pred.predicted_actions, item.expected_actions
            ),
            category=item.category,
            difficulty=item.difficulty,
        )

    def evaluate(
        self,
        predictions: list[Prediction] | list[dict[str, Any]],
    ) -> EvaluationResults:
        """Evaluate a list of predictions against the benchmark.

        Args:
            predictions: List of Prediction objects or raw dicts.

        Returns:
            Full evaluation results with overall, category, and difficulty breakdowns.
        """
        # Normalize input: accept dicts or Prediction objects.
        preds: dict[str, Prediction] = {}
        for p in predictions:
            if isinstance(p, dict):
                p = Prediction.from_dict(p)
            if p.id in preds:
                raise ValueError(f"Duplicate prediction ID: {p.id}")
            preds[p.id] = p

        # Score each benchmark item that has a prediction.
        item_scores: list[ItemScore] = []
        missing: list[str] = []

        for bid, item in self.benchmark.items():
            if bid not in preds:
                missing.append(bid)
                continue
            item_scores.append(self.score_item(item, preds[bid]))

        # Identify extra predictions not in benchmark.
        extra = [pid for pid in preds if pid not in self.benchmark]

        # Build breakdowns.
        by_category: dict[str, list[ItemScore]] = defaultdict(list)
        by_difficulty: dict[str, list[ItemScore]] = defaultdict(list)

        for s in item_scores:
            by_category[s.category].append(s)
            by_difficulty[s.difficulty].append(s)

        return EvaluationResults(
            overall=_aggregate(item_scores),
            by_category={k: _aggregate(v) for k, v in by_category.items()},
            by_difficulty={k: _aggregate(v) for k, v in by_difficulty.items()},
            item_scores=item_scores,
            missing_predictions=missing,
            extra_predictions=extra,
        )

    def evaluate_from_jsonl(self, path: str | Path) -> EvaluationResults:
        """Load predictions from a JSONL file and evaluate.

        Each line must be a JSON object with:
            id, predicted_intent, predicted_routing, predicted_actions
        """
        predictions: list[Prediction] = []
        path = Path(path)
        with path.open("r", encoding="utf-8") as f:
            for lineno, line in enumerate(f, start=1):
                line = line.strip()
                if not line:
                    continue
                try:
                    data = json.loads(line)
                except json.JSONDecodeError as e:
                    raise ValueError(
                        f"Invalid JSON at {path}:{lineno}: {e}"
                    ) from e
                try:
                    predictions.append(Prediction.from_dict(data))
                except KeyError as e:
                    raise ValueError(
                        f"Missing field {e} at {path}:{lineno}"
                    ) from e
        return self.evaluate(predictions)


# --- CLI ---


def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
    """Parse command-line arguments."""
    parser = argparse.ArgumentParser(
        description="Evaluate AI agent predictions against the insurance reliability benchmark.",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog=(
            "Example:\n"
            "  python evaluate.py \\\n"
            "    --benchmark data/test.jsonl \\\n"
            "    --predictions predictions.jsonl \\\n"
            "    --output results.json\n"
        ),
    )
    parser.add_argument(
        "--benchmark",
        required=True,
        type=Path,
        help="Path to benchmark JSONL file (ground truth).",
    )
    parser.add_argument(
        "--predictions",
        required=True,
        type=Path,
        help="Path to predictions JSONL file.",
    )
    parser.add_argument(
        "--output",
        type=Path,
        default=None,
        help="Path to write JSON results. Prints to stdout if omitted.",
    )
    parser.add_argument(
        "--quiet",
        action="store_true",
        help="Suppress the human-readable summary.",
    )
    return parser.parse_args(argv)


def main(argv: list[str] | None = None) -> None:
    """CLI entry point."""
    args = parse_args(argv)

    evaluator = BenchmarkEvaluator.from_jsonl(args.benchmark)
    results = evaluator.evaluate_from_jsonl(args.predictions)

    # Print summary unless suppressed.
    if not args.quiet:
        print(results.summary(), file=sys.stderr)

    # Write or print JSON results.
    results_dict = results.to_dict()
    json_str = json.dumps(results_dict, indent=2, ensure_ascii=False)

    if args.output:
        args.output.parent.mkdir(parents=True, exist_ok=True)
        args.output.write_text(json_str, encoding="utf-8")
        print(f"\nResults written to {args.output}", file=sys.stderr)
    else:
        print(json_str)


if __name__ == "__main__":
    main()