"""Output parsing for LocateAnything-3B bounding box responses."""

from __future__ import annotations

import re
from dataclasses import dataclass, field
from typing import Any

from src.config import COORD_MAX, DEFAULT_CONFIDENCE


@dataclass
class BBox:
    """A parsed bounding box in pixel coordinates."""

    x1: float
    y1: float
    x2: float
    y2: float
    confidence: float = DEFAULT_CONFIDENCE
    label: str = ""

    @property
    def width(self) -> float:
        return max(0.0, self.x2 - self.x1)

    @property
    def height(self) -> float:
        return max(0.0, self.y2 - self.y1)

    @property
    def area(self) -> float:
        return self.width * self.height

    @property
    def center(self) -> tuple[float, float]:
        return ((self.x1 + self.x2) / 2, (self.y1 + self.y2) / 2)

    def is_valid(self, img_w: int, img_h: int) -> bool:
        """Check if box is within image bounds and has positive area."""
        return (
            self.x1 >= 0
            and self.y1 >= 0
            and self.x2 <= img_w + 1
            and self.y2 <= img_h + 1
            and self.width > 1
            and self.height > 1
        )

    def clamp(self, img_w: int, img_h: int) -> BBox:
        """Return a clamped copy within image bounds."""
        return BBox(
            x1=max(0, min(self.x1, img_w)),
            y1=max(0, min(self.y1, img_h)),
            x2=max(0, min(self.x2, img_w)),
            y2=max(0, min(self.y2, img_h)),
            confidence=self.confidence,
            label=self.label,
        )

    def to_dict(self) -> dict[str, Any]:
        return {
            "x1": round(self.x1, 2),
            "y1": round(self.y1, 2),
            "x2": round(self.x2, 2),
            "y2": round(self.y2, 2),
            "width": round(self.width, 2),
            "height": round(self.height, 2),
            "confidence": self.confidence,
            "label": self.label,
        }


@dataclass
class ParseResult:
    """Structured result from parsing model output."""

    boxes: list[BBox] = field(default_factory=list)
    raw_output: str = ""
    parse_errors: list[str] = field(default_factory=list)

    @property
    def num_detections(self) -> int:
        return len(self.boxes)

    def to_dict(self) -> dict[str, Any]:
        return {
            "num_detections": self.num_detections,
            "boxes": [b.to_dict() for b in self.boxes],
            "raw_output": self.raw_output,
            "parse_errors": self.parse_errors,
        }


BOX_PATTERN_4 = re.compile(r"<box><(\d+)><(\d+)><(\d+)><(\d+)></box>")
BOX_PATTERN_4_ALT = re.compile(r"<box>\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*</box>")
BOX_PATTERN_2 = re.compile(r"<box><(\d+)><(\d+)></box>")


def _norm_to_pixel(val: int, scale: int) -> float:
    """Convert normalized [0, 1000] coordinate to pixel coordinate."""
    return val / COORD_MAX * scale


def parse_boxes(
    raw_output: str,
    image_width: int,
    image_height: int,
) -> ParseResult:
    """Parse model output into structured bounding boxes.

    The model outputs coordinates normalized to [0, 1000].
    This function converts them to pixel coordinates.
    """
    result = ParseResult(raw_output=raw_output)
    seen: set[tuple[float, float, float, float]] = set()

    for match in BOX_PATTERN_4.finditer(raw_output):
        try:
            x1 = _norm_to_pixel(int(match.group(1)), image_width)
            y1 = _norm_to_pixel(int(match.group(2)), image_height)
            x2 = _norm_to_pixel(int(match.group(3)), image_width)
            y2 = _norm_to_pixel(int(match.group(4)), image_height)
            key = (round(x1, 1), round(y1, 1), round(x2, 1), round(y2, 1))
            if key not in seen:
                seen.add(key)
                box = BBox(x1=x1, y1=y1, x2=x2, y2=y2)
                if box.is_valid(image_width, image_height):
                    result.boxes.append(box)
                else:
                    result.parse_errors.append(f"Out-of-bounds box discarded: {key}")
        except (ValueError, IndexError) as exc:
            result.parse_errors.append(f"Failed to parse box: {exc}")

    if not result.boxes:
        for match in BOX_PATTERN_4_ALT.finditer(raw_output):
            try:
                x1 = _norm_to_pixel(int(match.group(1)), image_width)
                y1 = _norm_to_pixel(int(match.group(2)), image_height)
                x2 = _norm_to_pixel(int(match.group(3)), image_width)
                y2 = _norm_to_pixel(int(match.group(4)), image_height)
                key = (round(x1, 1), round(y1, 1), round(x2, 1), round(y2, 1))
                if key not in seen:
                    seen.add(key)
                    box = BBox(x1=x1, y1=y1, x2=x2, y2=y2)
                    if box.is_valid(image_width, image_height):
                        result.boxes.append(box)
            except (ValueError, IndexError) as exc:
                result.parse_errors.append(f"Failed to parse alt box: {exc}")

    return result


def parse_points(
    raw_output: str,
    image_width: int,
    image_height: int,
) -> list[dict[str, float]]:
    """Parse model output into pixel-coordinate points."""
    points = []
    for match in BOX_PATTERN_2.finditer(raw_output):
        try:
            x = _norm_to_pixel(int(match.group(1)), image_width)
            y = _norm_to_pixel(int(match.group(2)), image_height)
            points.append({"x": round(x, 2), "y": round(y, 2)})
        except (ValueError, IndexError):
            pass
    return points