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edit//Qwen3-TTS-test//.venv//Lib//site-packages//transformers//models//grounding_dino//modular_grounding_dino.py

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+from typing import TYPE_CHECKING, Optional, Union
+
+import torch
+
+from transformers.models.detr.image_processing_detr_fast import DetrImageProcessorFast
+
+from ...image_transforms import center_to_corners_format
+from ...utils import (
+    TensorType,
+    logging,
+)
+
+
+if TYPE_CHECKING:
+    from .modeling_grounding_dino import GroundingDinoObjectDetectionOutput
+
+
+logger = logging.get_logger(__name__)
+
+
+def _scale_boxes(boxes, target_sizes):
+    """
+    Scale batch of bounding boxes to the target sizes.
+
+    Args:
+        boxes (`torch.Tensor` of shape `(batch_size, num_boxes, 4)`):
+            Bounding boxes to scale. Each box is expected to be in (x1, y1, x2, y2) format.
+        target_sizes (`list[tuple[int, int]]` or `torch.Tensor` of shape `(batch_size, 2)`):
+            Target sizes to scale the boxes to. Each target size is expected to be in (height, width) format.
+
+    Returns:
+        `torch.Tensor` of shape `(batch_size, num_boxes, 4)`: Scaled bounding boxes.
+    """
+
+    if isinstance(target_sizes, (list, tuple)):
+        image_height = torch.tensor([i[0] for i in target_sizes])
+        image_width = torch.tensor([i[1] for i in target_sizes])
+    elif isinstance(target_sizes, torch.Tensor):
+        image_height, image_width = target_sizes.unbind(1)
+    else:
+        raise TypeError("`target_sizes` must be a list, tuple or torch.Tensor")
+
+    scale_factor = torch.stack([image_width, image_height, image_width, image_height], dim=1)
+    scale_factor = scale_factor.unsqueeze(1).to(boxes.device)
+    boxes = boxes * scale_factor
+    return boxes
+
+
+class GroundingDinoImageProcessorFast(DetrImageProcessorFast):
+    def post_process_object_detection(
+        self,
+        outputs: "GroundingDinoObjectDetectionOutput",
+        threshold: float = 0.1,
+        target_sizes: Optional[Union[TensorType, list[tuple]]] = None,
+    ):
+        """
+        Converts the raw output of [`GroundingDinoForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
+        bottom_right_x, bottom_right_y) format.
+
+        Args:
+            outputs ([`GroundingDinoObjectDetectionOutput`]):
+                Raw outputs of the model.
+            threshold (`float`, *optional*, defaults to 0.1):
+                Score threshold to keep object detection predictions.
+            target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*):
+                Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size
+                `(height, width)` of each image in the batch. If unset, predictions will not be resized.
+
+        Returns:
+            `list[Dict]`: A list of dictionaries, each dictionary containing the following keys:
+            - "scores": The confidence scores for each predicted box on the image.
+            - "labels": Indexes of the classes predicted by the model on the image.
+            - "boxes": Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format.
+        """
+        batch_logits, batch_boxes = outputs.logits, outputs.pred_boxes
+        batch_size = len(batch_logits)
+
+        if target_sizes is not None and len(target_sizes) != batch_size:
+            raise ValueError("Make sure that you pass in as many target sizes as images")
+
+        # batch_logits of shape (batch_size, num_queries, num_classes)
+        batch_class_logits = torch.max(batch_logits, dim=-1)
+        batch_scores = torch.sigmoid(batch_class_logits.values)
+        batch_labels = batch_class_logits.indices
+
+        # Convert to [x0, y0, x1, y1] format
+        batch_boxes = center_to_corners_format(batch_boxes)
+
+        # Convert from relative [0, 1] to absolute [0, height] coordinates
+        if target_sizes is not None:
+            batch_boxes = _scale_boxes(batch_boxes, target_sizes)
+
+        results = []
+        for scores, labels, boxes in zip(batch_scores, batch_labels, batch_boxes):
+            keep = scores > threshold
+            scores = scores[keep]
+            labels = labels[keep]
+            boxes = boxes[keep]
+            results.append({"scores": scores, "labels": labels, "boxes": boxes})
+
+        return results
+
+    def post_process(self):
+        raise NotImplementedError("Post-processing is not implemented for Grounding-Dino yet.")
+
+    def post_process_segmentation(self):
+        raise NotImplementedError("Segmentation post-processing is not implemented for Grounding-Dino yet.")
+
+    def post_process_instance(self):
+        raise NotImplementedError("Instance post-processing is not implemented for Grounding-Dino yet.")
+
+    def post_process_panoptic(self):
+        raise NotImplementedError("Panoptic post-processing is not implemented for Grounding-Dino yet.")
+
+    def post_process_instance_segmentation(self):
+        raise NotImplementedError("Segmentation post-processing is not implemented for Grounding-Dino yet.")
+
+    def post_process_semantic_segmentation(self):
+        raise NotImplementedError("Semantic segmentation post-processing is not implemented for Grounding-Dino yet.")
+
+    def post_process_panoptic_segmentation(self):
+        raise NotImplementedError("Panoptic segmentation post-processing is not implemented for Grounding-Dino yet.")
+
+
+__all__ = ["GroundingDinoImageProcessorFast"]