# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

import torch

from ultralytics.models.yolo.detect.predict import DetectionPredictor
from ultralytics.utils import ops


class NASPredictor(DetectionPredictor):
    """Ultralytics YOLO NAS Predictor for object detection.

    This class extends the DetectionPredictor from Ultralytics engine and is responsible for post-processing the raw
    predictions generated by the YOLO NAS models. It applies operations like non-maximum suppression and scaling the
    bounding boxes to fit the original image dimensions.

    Attributes:
        args (Namespace): Namespace containing various configurations for post-processing including confidence
            threshold, IoU threshold, agnostic NMS flag, maximum detections, and class filtering options.
        model (torch.nn.Module): The YOLO NAS model used for inference.
        batch (list): Batch of inputs for processing.

    Examples:
        >>> from ultralytics import NAS
        >>> model = NAS("yolo_nas_s")
        >>> predictor = model.predictor

        Assume that raw_preds, img, orig_imgs are available
        >>> results = predictor.postprocess(raw_preds, img, orig_imgs)

    Notes:
        Typically, this class is not instantiated directly. It is used internally within the NAS class.
    """

    def postprocess(self, preds_in, img, orig_imgs):
        """Postprocess NAS model predictions to generate final detection results.

        This method takes raw predictions from a YOLO NAS model, converts bounding box formats, and applies
        post-processing operations to generate the final detection results compatible with Ultralytics result
        visualization and analysis tools.

        Args:
            preds_in (list): Raw predictions from the NAS model, typically containing bounding boxes and class scores.
            img (torch.Tensor): Input image tensor that was fed to the model, with shape (B, C, H, W).
            orig_imgs (list | torch.Tensor | np.ndarray): Original images before preprocessing, used for scaling
                coordinates back to original dimensions.

        Returns:
            (list): List of Results objects containing the processed predictions for each image in the batch.

        Examples:
            >>> predictor = NAS("yolo_nas_s").predictor
            >>> results = predictor.postprocess(raw_preds, img, orig_imgs)
        """
        boxes = ops.xyxy2xywh(preds_in[0][0])  # Convert bounding boxes from xyxy to xywh format
        preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)  # Concatenate boxes with class scores
        return super().postprocess(preds, img, orig_imgs)
