from __future__ import annotations

import time
from typing import Any

import numpy as np

from app.ocr.base import OcrLine, OcrResult, crop_bbox, prepare_ocr_image


class PaddleOcrEngine:
    name = "paddle"

    def __init__(self, config: dict) -> None:
        self.config = config
        self.load_error: str | None = None
        self.ocr: Any = None
        self._load()

    def _load(self) -> None:
        try:
            from paddleocr import PaddleOCR
        except Exception as exc:
            self.load_error = f"Nie mozna zaimportowac PaddleOCR: {exc}"
            return

        paddle_cfg = dict(self.config.get("paddle", {}))
        paddle_cfg.setdefault("lang", self.config.get("language", "en"))
        try:
            self.ocr = PaddleOCR(**paddle_cfg)
        except Exception as exc:
            self.load_error = f"Nie mozna zaladowac PaddleOCR: {exc}"

    def read_label(self, frame_bgr: np.ndarray, bbox: tuple[int, int, int, int]) -> OcrResult:
        started = time.perf_counter()
        if self.ocr is None:
            return OcrResult(
                error=self.load_error or "PaddleOCR nie jest zaladowany",
                elapsed_ms=self._elapsed_ms(started),
                engine=self.name,
            )

        margin = int(self.config.get("margin", 0))
        roi = crop_bbox(frame_bgr, bbox, margin=margin)
        if roi is None:
            return OcrResult(
                error="Nieprawidlowy bbox OCR",
                elapsed_ms=self._elapsed_ms(started),
                engine=self.name,
            )

        preprocess_config = {
            **self.config,
            "threshold": bool(self.config.get("paddle_threshold", False)),
        }
        image = prepare_ocr_image(roi, preprocess_config)
        try:
            raw_result = self._run_ocr(image)
        except Exception as exc:
            return OcrResult(
                error=f"Blad PaddleOCR: {exc}",
                elapsed_ms=self._elapsed_ms(started),
                engine=self.name,
            )

        lines = self._parse_lines(raw_result)
        text = "\n".join(line.text for line in lines)
        confidences = [line.confidence for line in lines if line.confidence is not None]
        confidence = sum(confidences) / len(confidences) if confidences else None
        return OcrResult(
            text=text,
            confidence=confidence,
            lines=lines,
            elapsed_ms=self._elapsed_ms(started),
            engine=self.name,
        )

    def _run_ocr(self, image: np.ndarray) -> Any:
        if hasattr(self.ocr, "predict"):
            return self.ocr.predict(image)
        try:
            return self.ocr.ocr(image, cls=bool(self.config.get("use_angle_cls", True)))
        except TypeError:
            return self.ocr.ocr(image)

    def _parse_lines(self, raw_result: Any) -> list[OcrLine]:
        if raw_result is None:
            return []

        lines: list[OcrLine] = []
        for item in self._iter_result_items(raw_result):
            parsed = self._parse_item(item)
            if parsed is not None and parsed.text.strip():
                lines.append(parsed)
        return lines

    def _iter_result_items(self, raw_result: Any) -> list[Any]:
        if isinstance(raw_result, dict):
            texts = raw_result.get("rec_texts") or raw_result.get("texts")
            scores = raw_result.get("rec_scores") or raw_result.get("scores") or []
            boxes = raw_result.get("rec_polys") or raw_result.get("dt_polys") or raw_result.get("boxes") or []
            if texts:
                return [
                    (boxes[index] if index < len(boxes) else None, (text, scores[index] if index < len(scores) else None))
                    for index, text in enumerate(texts)
                ]
            return []

        if isinstance(raw_result, list) and len(raw_result) == 1 and isinstance(raw_result[0], list):
            return raw_result[0]
        if isinstance(raw_result, list):
            items = []
            for result in raw_result:
                if isinstance(result, dict):
                    items.extend(self._iter_result_items(result))
                elif isinstance(result, list):
                    items.extend(result)
                else:
                    items.append(result)
            return items
        return [raw_result]

    def _parse_item(self, item: Any) -> OcrLine | None:
        if not isinstance(item, (list, tuple)):
            return None

        if len(item) >= 2 and isinstance(item[1], (list, tuple)) and item[1]:
            text = str(item[1][0])
            confidence = self._to_float(item[1][1]) if len(item[1]) > 1 else None
            bbox = self._to_bbox(item[0])
            return OcrLine(text=text, confidence=confidence, bbox=bbox)

        if len(item) >= 2 and isinstance(item[0], str):
            return OcrLine(text=str(item[0]), confidence=self._to_float(item[1]))

        return None

    def _to_float(self, value: Any) -> float | None:
        try:
            return float(value)
        except (TypeError, ValueError):
            return None

    def _to_bbox(self, value: Any) -> list[list[float]] | None:
        if value is None:
            return None
        try:
            return [[float(point[0]), float(point[1])] for point in value]
        except (TypeError, ValueError, IndexError):
            return None

    def _elapsed_ms(self, started: float) -> float:
        return (time.perf_counter() - started) * 1000.0