[`SAM`] Fixes pipeline and adds a dummy pipeline test (#23684)

* add a dummy pipeline test

* change test name

src/transformers/models/sam/image_processing_sam.py

@@ -934,7 +934,7 @@ def _generate_crop_boxes(
     cropped_images, point_grid_per_crop = _generate_crop_images(
         crop_boxes, image, points_grid, layer_idxs, target_size, original_size
     )
-
+    crop_boxes = np.array(crop_boxes)
     crop_boxes = crop_boxes.astype(np.float32)
     points_per_crop = np.array([point_grid_per_crop])
     points_per_crop = np.transpose(points_per_crop, axes=(0, 2, 1, 3))

tests/models/sam/test_modeling_sam.py

@@ -20,7 +20,7 @@ import unittest
 
 import requests
 
-from transformers import SamConfig, SamMaskDecoderConfig, SamPromptEncoderConfig, SamVisionConfig
+from transformers import SamConfig, SamMaskDecoderConfig, SamPromptEncoderConfig, SamVisionConfig, pipeline
 from transformers.testing_utils import require_torch, slow, torch_device
 from transformers.utils import is_torch_available, is_vision_available
 
@@ -751,3 +751,9 @@ class SamModelIntegrationTest(unittest.TestCase):
         iou_scores = outputs.iou_scores.cpu()
         self.assertTrue(iou_scores.shape == (1, 3, 3))
         torch.testing.assert_allclose(iou_scores, EXPECTED_IOU, atol=1e-4, rtol=1e-4)
+
+    def test_dummy_pipeline_generation(self):
+        generator = pipeline("mask-generation", model="facebook/sam-vit-base", device=torch_device)
+        raw_image = prepare_image()
+
+        _ = generator(raw_image, points_per_batch=64)