"""Inference for CodeBERT SQL error cross-encoder."""

from __future__ import annotations

import argparse
import json
from pathlib import Path
from typing import List, Optional, Union

import numpy as np
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from src.codebert_formatting import format_cross_encoder_input, sql_queries_equivalent
from src.device_utils import get_device
from src.codebert_labels import load_codebert_labels, multihot_to_label_names
from src.hf_metrics import sigmoid

PROJECT_ROOT = Path(__file__).resolve().parent.parent
DEFAULT_MODEL_DIR = PROJECT_ROOT / "models" / "codebert-cross-encoder"


def _is_hub_id(model_dir: Union[str, Path]) -> bool:
    text = str(model_dir)
    local = Path(text)
    return "/" in text and not local.exists()


class CodeBERTSQLErrorClassifier:
    """CodeBERT cross-encoder inference wrapper."""

    def __init__(
        self,
        model_dir: Union[str, Path] = DEFAULT_MODEL_DIR,
        threshold: float = 0.5,
        device: Optional[str] = None,
    ):
        self.hub_id = str(model_dir) if _is_hub_id(model_dir) else None
        self.model_dir = Path(model_dir) if not self.hub_id else None
        self.threshold = threshold
        # MPS inference can be flaky for some ops; CPU is reliable on Mac
        self.device = device or (
            "cuda" if torch.cuda.is_available() else "cpu"
        )
        model_ref = self.hub_id or str(self.model_dir)

        if self.hub_id:
            self.label_list = load_codebert_labels()
            self.max_length = 512
        else:
            config_path = self.model_dir / "label_config.json"
            if config_path.exists():
                with open(config_path) as f:
                    cfg = json.load(f)
                self.label_list = cfg.get("labels", load_codebert_labels())
                self.threshold = cfg.get("threshold", threshold)
                self.max_length = cfg.get("max_length", 512)
            else:
                self.label_list = load_codebert_labels()
                self.max_length = 512

        self.tokenizer = AutoTokenizer.from_pretrained(model_ref)
        self.model = AutoModelForSequenceClassification.from_pretrained(
            model_ref
        ).to(self.device)
        self.model.eval()

    def predict(
        self,
        question: str,
        schema: str,
        student_sql: str,
        correct_sql: str,
        threshold: Optional[float] = None,
        top_k: int = 5,
    ) -> dict:
        thr = threshold if threshold is not None else self.threshold

        if sql_queries_equivalent(student_sql, correct_sql):
            return {
                "error_labels": [],
                "probabilities": {name: 0.0 for name in self.label_list},
                "top_k": [
                    {"label": name, "probability": 0.0}
                    for name in self.label_list[:5]
                ],
                "primary_label": "NO_ERROR",
                "primary_confidence": 1.0,
                "match_detected": True,
            }

        text = format_cross_encoder_input(
            question=question,
            schema=schema,
            student_sql=student_sql,
            correct_sql=correct_sql,
        )
        encoded = self.tokenizer(
            text,
            truncation=True,
            max_length=self.max_length,
            padding=True,
            return_tensors="pt",
        ).to(self.device)

        with torch.no_grad():
            logits = self.model(**encoded).logits.cpu().numpy()[0]

        probs = sigmoid(logits)
        predicted = multihot_to_label_names(probs, self.label_list, threshold=thr)

        ranked = sorted(
            zip(self.label_list, probs.tolist()),
            key=lambda x: x[1],
            reverse=True,
        )[:top_k]

        top_label, top_prob = ranked[0]
        if top_prob >= thr:
            primary_label = top_label
            primary_confidence = float(top_prob)
        else:
            primary_label = "NO_ERROR"
            primary_confidence = 1.0 - float(top_prob)

        return {
            "error_labels": predicted,
            "probabilities": {name: float(p) for name, p in ranked},
            "top_k": [
                {"label": name, "probability": float(p)} for name, p in ranked
            ],
            "primary_label": primary_label,
            "primary_confidence": primary_confidence,
            "match_detected": False,
        }

    def predict_batch(
        self,
        examples: List[dict],
        batch_size: int = 16,
    ) -> List[dict]:
        results = []
        for i in range(0, len(examples), batch_size):
            chunk = examples[i : i + batch_size]
            texts = [
                format_cross_encoder_input(
                    question=x["question"],
                    schema=x["schema"],
                    student_sql=x["student_sql"],
                    correct_sql=x["correct_sql"],
                )
                for x in chunk
            ]
            encoded = self.tokenizer(
                texts,
                truncation=True,
                max_length=self.max_length,
                padding=True,
                return_tensors="pt",
            ).to(self.device)

            with torch.no_grad():
                logits = self.model(**encoded).logits.cpu().numpy()

            for j, row in enumerate(logits):
                probs = sigmoid(row)
                results.append(
                    {
                        "error_labels": multihot_to_label_names(
                            probs, self.label_list, self.threshold
                        ),
                        "primary_label": self.label_list[int(np.argmax(probs))],
                        "primary_confidence": float(np.max(probs)),
                    }
                )
        return results


def main() -> None:
    parser = argparse.ArgumentParser(description="CodeBERT SQL error inference")
    parser.add_argument("--model-dir", type=Path, default=DEFAULT_MODEL_DIR)
    parser.add_argument("--question", type=str, required=True)
    parser.add_argument("--schema", type=str, required=True)
    parser.add_argument("--student-sql", type=str, required=True)
    parser.add_argument("--correct-sql", type=str, required=True)
    parser.add_argument("--threshold", type=float, default=0.5)
    args = parser.parse_args()

    clf = CodeBERTSQLErrorClassifier(args.model_dir, threshold=args.threshold)
    result = clf.predict(
        question=args.question,
        schema=args.schema,
        student_sql=args.student_sql,
        correct_sql=args.correct_sql,
    )
    print(json.dumps(result, indent=2))


if __name__ == "__main__":
    main()