"""Inference API for SQL error classification."""

from __future__ import annotations

import argparse
import json
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import List, Optional

from src.categories import id_to_name, load_categories
from src.model import DEFAULT_MODEL_PATH, combine_features, load_model
from src.cross_encoder_model import (
    CrossEncoderClassifier,
    FineTunedCrossEncoderClassifier,
)
from src.multi_tower_model import MultiTowerClassifier, QueryContext

CONTEXT_MODELS = (
    CrossEncoderClassifier,
    FineTunedCrossEncoderClassifier,
    MultiTowerClassifier,
)


@dataclass
class Prediction:
    label_id: int
    label_name: str
    confidence: float
    top_k: List[dict]
    similarities: Optional[dict] = None
    pair_scores: Optional[dict] = None


class SQLErrorClassifier:
    """Classifier wrapper for playground integration."""

    def __init__(self, model_path: Path = DEFAULT_MODEL_PATH):
        self.model = load_model(model_path)
        self.label_map = id_to_name(load_categories())

    def predict(
        self,
        query: str,
        error_message: Optional[str] = None,
        schema: Optional[str] = None,
        question: Optional[str] = None,
        correct_query: Optional[str] = None,
        top_k: int = 3,
    ) -> Prediction:
        if isinstance(self.model, CONTEXT_MODELS):
            if not all([schema, question, correct_query]):
                raise ValueError(
                    "context models require schema, question, and correct_query"
                )
            ctx = QueryContext(
                question=question,
                schema=schema,
                correct_query=correct_query,
                student_query=query,
                error_message=error_message,
            )
            proba = self.model.predict_proba([ctx])[0]
            similarities = (
                self.model.explain_similarities(ctx)
                if isinstance(self.model, MultiTowerClassifier)
                else None
            )
            pair_scores = (
                self.model.explain_pair_scores(ctx)
                if isinstance(self.model, CrossEncoderClassifier)
                else None
            )
        else:
            pair_scores = None
            similarities = None
            text = combine_features(
                queries=[query],
                error_messages=[error_message] if error_message else None,
                schemas=[schema] if schema else None,
                questions=[question] if question else None,
            )[0]
            proba = self.model.predict_proba([text])[0]
            similarities = None

        classes = self.model.classes_
        ranked = sorted(zip(classes, proba), key=lambda x: x[1], reverse=True)
        best_id = int(ranked[0][0])

        return Prediction(
            label_id=best_id,
            label_name=self.label_map[best_id],
            confidence=float(ranked[0][1]),
            top_k=[
                {
                    "label_id": int(cls),
                    "label_name": self.label_map[int(cls)],
                    "confidence": float(p),
                }
                for cls, p in ranked[:top_k]
            ],
            similarities=similarities,
            pair_scores=pair_scores,
        )


def main() -> None:
    parser = argparse.ArgumentParser(description="Classify SQL error type")
    parser.add_argument("--query", type=str, required=True)
    parser.add_argument("--correct-query", type=str, default=None)
    parser.add_argument("--error-message", type=str, default=None)
    parser.add_argument("--schema", type=str, default=None)
    parser.add_argument("--question", type=str, default=None)
    parser.add_argument("--model", type=Path, default=DEFAULT_MODEL_PATH)
    parser.add_argument("--top-k", type=int, default=3)
    args = parser.parse_args()

    clf = SQLErrorClassifier(args.model)
    result = clf.predict(
        args.query,
        args.error_message,
        args.schema,
        args.question,
        args.correct_query,
        top_k=args.top_k,
    )
    print(json.dumps(asdict(result), indent=2))


if __name__ == "__main__":
    main()