"""Cross-encoder architecture for SQL error classification."""

from __future__ import annotations

from dataclasses import dataclass
from pathlib import Path
from typing import List, Optional, Tuple

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler

from src.multi_tower_model import QueryContext, contexts_from_dataframe
from src.sql_features import extract_sql_features

DEFAULT_CROSS_ENCODER = "cross-encoder/ms-marco-MiniLM-L6-v2"
DEFAULT_FINETUNED_CE = "cross-encoder/ms-marco-MiniLM-L6-v2"

PAIR_NAMES = (
    "intent_vs_student",
    "reference_vs_student",
    "intent_vs_reference",
)


@dataclass(frozen=True)
class CrossEncoderPair:
    name: str
    text_a: str
    text_b: str


def _intent_text(ctx: QueryContext) -> str:
    return f"QUESTION: {ctx.question} SCHEMA: {ctx.schema}"


def _reference_text(ctx: QueryContext) -> str:
    return f"REFERENCE: {ctx.correct_query}"


def _student_text(ctx: QueryContext) -> str:
    parts = [f"STUDENT: {ctx.student_query}"]
    if ctx.error_message:
        parts.append(f"ERROR: {ctx.error_message}")
    return " ".join(parts)


def _context_text(ctx: QueryContext) -> str:
    """Full task context for fine-tuned cross-encoder."""
    return (
        f"QUESTION: {ctx.question} "
        f"SCHEMA: {ctx.schema} "
        f"REFERENCE: {ctx.correct_query}"
    )


def build_pairs(ctx: QueryContext) -> List[CrossEncoderPair]:
    intent, reference, student = (
        _intent_text(ctx),
        _reference_text(ctx),
        _student_text(ctx),
    )
    return [
        CrossEncoderPair("intent_vs_student", intent, student),
        CrossEncoderPair("reference_vs_student", reference, student),
        CrossEncoderPair("intent_vs_reference", intent, reference),
    ]


class CrossEncoderClassifier:
    """
    Hybrid cross-encoder: frozen pairwise relevance + linear head.

    Unlike bi-encoders (multi-tower), the cross-encoder attends jointly over
    each (context, student) pair — better for logical and filtering errors.

    Three pairs are scored:
      1. intent vs student     — does the query address the question?
      2. reference vs student  — how far is the student from the answer?
      3. intent vs reference   — task-answer alignment baseline

    Pair scores + SQL rule features → LogisticRegression → 15 classes.
    """

    def __init__(
        self,
        cross_encoder_name: str = DEFAULT_CROSS_ENCODER,
        batch_size: int = 32,
        max_length: int = 512,
    ):
        self.cross_encoder_name = cross_encoder_name
        self.batch_size = batch_size
        self.max_length = max_length
        self.cross_encoder = None
        self.scaler = StandardScaler()
        self.clf = LogisticRegression(
            max_iter=1000,
            solver="lbfgs",
            class_weight="balanced",
            random_state=42,
        )
        self.classes_: Optional[np.ndarray] = None

    def _load_cross_encoder(self):
        if self.cross_encoder is None:
            from sentence_transformers import CrossEncoder

            self.cross_encoder = CrossEncoder(
                self.cross_encoder_name,
                max_length=self.max_length,
            )

    def _pair_batches(self, contexts: List[QueryContext]) -> List[List[Tuple[str, str]]]:
        """One batch list per pair type across all contexts."""
        pair_lists: List[List[Tuple[str, str]]] = [[], [], []]
        for ctx in contexts:
            pairs = build_pairs(ctx)
            for i, pair in enumerate(pairs):
                pair_lists[i].append((pair.text_a, pair.text_b))
        return pair_lists

    def _score_pairs(
        self,
        contexts: List[QueryContext],
        show_progress: bool = False,
    ) -> np.ndarray:
        self._load_cross_encoder()
        pair_batches = self._pair_batches(contexts)
        scores = []
        for batch in pair_batches:
            raw = self.cross_encoder.predict(
                batch,
                batch_size=self.batch_size,
                show_progress_bar=show_progress,
            )
            scores.append(np.asarray(raw, dtype=np.float64).reshape(-1, 1))
        return np.hstack(scores)  # (n, 3)

    def _build_features(
        self,
        contexts: List[QueryContext],
        show_progress: bool = False,
    ) -> np.ndarray:
        pair_scores = self._score_pairs(contexts, show_progress=show_progress)
        s_is, s_rs, s_ir = pair_scores[:, 0], pair_scores[:, 1], pair_scores[:, 2]

        derived = np.column_stack(
            [
                s_rs - s_is,          # reference closer than intent?
                s_is - s_ir,          # student-intent gap vs baseline
                s_rs - s_ir,          # student-reference gap vs baseline
                s_is * s_rs,          # interaction
                np.abs(s_rs - s_is),  # intent-reference disagreement
            ]
        )

        sql_feats = np.array(
            [extract_sql_features(c.student_query, c.correct_query) for c in contexts],
            dtype=np.float64,
        )

        return np.hstack([pair_scores, derived, sql_feats])

    def _prepare_features(self, contexts: List[QueryContext]) -> np.ndarray:
        X = self.scaler.transform(self._build_features(contexts))
        return np.nan_to_num(X, nan=0.0, posinf=1e3, neginf=-1e3)

    def fit(self, contexts: List[QueryContext], y: np.ndarray) -> "CrossEncoderClassifier":
        X = self._build_features(contexts, show_progress=True)
        X = self.scaler.fit_transform(X)
        X = np.nan_to_num(X, nan=0.0, posinf=1e3, neginf=-1e3)
        self.clf.fit(X, y)
        self.classes_ = self.clf.classes_
        return self

    def predict(self, contexts: List[QueryContext]) -> np.ndarray:
        return self.clf.predict(self._prepare_features(contexts))

    def predict_proba(self, contexts: List[QueryContext]) -> np.ndarray:
        return self.clf.predict_proba(self._prepare_features(contexts))

    def explain_pair_scores(self, ctx: QueryContext) -> dict:
        scores = self._score_pairs([ctx])[0]
        return {
            PAIR_NAMES[0]: float(scores[0]),
            PAIR_NAMES[1]: float(scores[1]),
            PAIR_NAMES[2]: float(scores[2]),
        }


class FineTunedCrossEncoderClassifier:
    """
    End-to-end fine-tuned cross-encoder (highest accuracy).

    Single cross-attention pass over [task_context | student_query] with
    num_labels=15. Slower to train; best on smaller high-quality datasets.
    """

    def __init__(
        self,
        cross_encoder_name: str = DEFAULT_FINETUNED_CE,
        batch_size: int = 16,
        max_length: int = 512,
        num_labels: int = 15,
    ):
        self.cross_encoder_name = cross_encoder_name
        self.batch_size = batch_size
        self.max_length = max_length
        self.num_labels = num_labels
        self.model = None
        self.classes_: Optional[np.ndarray] = None

    def _load_model(self, num_labels: Optional[int] = None):
        if self.model is None:
            from sentence_transformers import CrossEncoder

            self.model = CrossEncoder(
                self.cross_encoder_name,
                num_labels=num_labels or self.num_labels,
                max_length=self.max_length,
            )

    def _to_examples(self, contexts: List[QueryContext], labels: Optional[np.ndarray] = None):
        from sentence_transformers import InputExample

        examples = []
        for i, ctx in enumerate(contexts):
            label = float(labels[i]) if labels is not None else 0.0
            examples.append(
                InputExample(
                    texts=[_context_text(ctx), _student_text(ctx)],
                    label=label,
                )
            )
        return examples

    def fit(
        self,
        contexts: List[QueryContext],
        y: np.ndarray,
        epochs: int = 1,
        warmup_steps: int = 100,
        output_path: Optional[Path] = None,
    ) -> "FineTunedCrossEncoderClassifier":
        from torch.utils.data import DataLoader

        self._load_model(num_labels=len(np.unique(y)))
        train_examples = self._to_examples(contexts, y)
        loader = DataLoader(
            train_examples,
            shuffle=True,
            batch_size=self.batch_size,
        )
        self.model.fit(
            train_dataloader=loader,
            epochs=epochs,
            warmup_steps=min(warmup_steps, max(10, len(train_examples) // 10)),
            show_progress_bar=True,
            output_path=str(output_path) if output_path else None,
        )
        self.classes_ = np.sort(np.unique(y))
        return self

    def predict(self, contexts: List[QueryContext]) -> np.ndarray:
        self._load_model()
        pairs = [[_context_text(c), _student_text(c)] for c in contexts]
        logits = self.model.predict(
            pairs,
            batch_size=self.batch_size,
            show_progress_bar=False,
            convert_to_numpy=True,
        )
        logits = np.asarray(logits)
        if logits.ndim == 1:
            return logits.astype(int)
        return logits.argmax(axis=1)

    def predict_proba(self, contexts: List[QueryContext]) -> np.ndarray:
        self._load_model()
        pairs = [[_context_text(c), _student_text(c)] for c in contexts]
        logits = self.model.predict(
            pairs,
            batch_size=self.batch_size,
            show_progress_bar=False,
            convert_to_numpy=True,
        )
        logits = np.asarray(logits, dtype=np.float64)
        if logits.ndim == 1:
            # binary fallback
            probs = np.zeros((len(contexts), len(self.classes_)))
            for i, pred in enumerate(logits.astype(int)):
                idx = np.where(self.classes_ == pred)[0][0]
                probs[i, idx] = 1.0
            return probs
        # softmax
        exp = np.exp(logits - logits.max(axis=1, keepdims=True))
        return exp / exp.sum(axis=1, keepdims=True)

    def save(self, path: Path) -> Path:
        path.mkdir(parents=True, exist_ok=True)
        self._load_model()
        self.model.save(str(path))
        return path

    @classmethod
    def load(cls, path: Path) -> "FineTunedCrossEncoderClassifier":
        from sentence_transformers import CrossEncoder

        instance = cls()
        instance.model = CrossEncoder(str(path))
        instance.classes_ = np.arange(instance.model.num_labels)
        return instance