import logging
from typing import Sequence

import numpy as np
import torch
from transformers import DetrForObjectDetection, DetrImageProcessor

from models.detectors.base import DetectionResult, ObjectDetector


class DetrDetector(ObjectDetector):
    """Wrapper around facebook/detr-resnet-50 for mission-aligned detection."""

    MODEL_NAME = "facebook/detr-resnet-50"

    def __init__(self, score_threshold: float = 0.3) -> None:
        self.name = "detr_resnet50"
        self.score_threshold = score_threshold
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        logging.info("Loading %s onto %s", self.MODEL_NAME, self.device)
        self.processor = DetrImageProcessor.from_pretrained(self.MODEL_NAME)
        self.model = DetrForObjectDetection.from_pretrained(self.MODEL_NAME)
        self.model.to(self.device)
        self.model.eval()

    def predict(self, frame: np.ndarray, queries: Sequence[str]) -> DetectionResult:
        inputs = self.processor(images=frame, return_tensors="pt")
        inputs = {key: value.to(self.device) for key, value in inputs.items()}
        with torch.no_grad():
            outputs = self.model(**inputs)
        target_sizes = torch.tensor([frame.shape[:2]], device=self.device)
        processed = self.processor.post_process_object_detection(
            outputs,
            threshold=self.score_threshold,
            target_sizes=target_sizes,
        )[0]
        boxes = processed["boxes"].cpu().numpy()
        scores = processed["scores"].cpu().tolist()
        labels = processed["labels"].cpu().tolist()
        label_names = [
            self.model.config.id2label.get(int(idx), f"class_{idx}") for idx in labels
        ]
        return DetectionResult(
            boxes=boxes,
            scores=scores,
            labels=labels,
            label_names=label_names,
        )