chore: import upstream snapshot with attribution

tests/models/sam3/test_processing_sam3.py

@@ -0,0 +1,114 @@
+# Copyright 2025 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from transformers import AutoTokenizer
+from transformers.models.sam3.processing_sam3 import Sam3Processor
+from transformers.testing_utils import require_torch, require_vision
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+@require_torch
+@require_vision
+class Sam3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Sam3Processor
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        return AutoTokenizer.from_pretrained(
+            "hf-internal-testing/tiny-processor-clip", max_length=32, model_max_length=32
+        )
+
+    @classmethod
+    def _setup_image_processor(cls):
+        from transformers.models.sam3.image_processing_sam3 import Sam3ImageProcessor
+
+        # Default size=1008×1008 allocates large tensors; use tiny sizes for tests
+        return Sam3ImageProcessor(size={"height": 64, "width": 64}, mask_size={"height": 16, "width": 16})
+
+    # Sam3Processor has a custom non-standard __call__ signature (no chat template, extra
+    # prompting args like input_boxes). Skip mixin tests that assume a standard VLM interface.
+    def test_chat_template_save_loading(self):
+        self.skipTest("Sam3Processor does not use a chat template")
+
+    def test_model_input_names(self):
+        self.skipTest("Sam3Processor outputs extra keys (e.g. original_sizes) beyond model_input_names")
+
+    def test_tokenizer_defaults(self):
+        self.skipTest("Sam3Processor always pads tokenizer output to max_length=32")
+
+    def test_processor_text_has_no_visual(self):
+        self.skipTest("Sam3Processor has a custom interface, not a standard VLM text+image interface")
+
+    def test_processor_with_multiple_inputs(self):
+        self.skipTest("Sam3Processor has a custom interface, not a standard VLM text+image interface")
+
+    # --- Sam3-specific tests ---
+
+    def test_input_boxes_default_labels_mixed_batch(self):
+        # Regression test for https://github.com/huggingface/transformers/issues/45059:
+        # None entries should get pad label (-10), real entries should get positive label (1).
+        processor = self.get_processor()
+        images = self.prepare_image_inputs(batch_size=2)
+        inputs = processor(
+            images=images,
+            text=["cat", None],
+            input_boxes=[None, [[100, 100, 200, 200]]],
+            return_tensors="pt",
+        )
+        self.assertIn("input_boxes", inputs)
+        self.assertIn("input_boxes_labels", inputs)
+
+        # The None entry (index 0) should have label -10 (pad value)
+        self.assertEqual(inputs["input_boxes_labels"][0, 0].item(), -10)
+        # The real entry (index 1) should have label 1 (positive)
+        self.assertEqual(inputs["input_boxes_labels"][1, 0].item(), 1)
+
+    def test_input_boxes_default_labels_all_real(self):
+        processor = self.get_processor()
+        images = self.prepare_image_inputs(batch_size=2)
+        inputs = processor(
+            images=images,
+            text=["cat", "dog"],
+            input_boxes=[[[50, 50, 150, 150]], [[200, 200, 300, 300]]],
+            return_tensors="pt",
+        )
+        self.assertIn("input_boxes_labels", inputs)
+        self.assertTrue((inputs["input_boxes_labels"] == 1).all())
+
+    def test_no_input_boxes_omits_labels(self):
+        processor = self.get_processor()
+        images = self.prepare_image_inputs(batch_size=1)
+        inputs = processor(
+            images=images,
+            text=["cat"],
+            return_tensors="pt",
+        )
+        self.assertNotIn("input_boxes", inputs)
+        self.assertNotIn("input_boxes_labels", inputs)
+
+    def test_user_provided_labels_preserved(self):
+        processor = self.get_processor()
+        images = self.prepare_image_inputs(batch_size=2)
+        inputs = processor(
+            images=images,
+            text=["cat", "dog"],
+            input_boxes=[[[50, 50, 150, 150]], [[200, 200, 300, 300]]],
+            input_boxes_labels=[[1], [0]],
+            return_tensors="pt",
+        )
+        self.assertEqual(inputs["input_boxes_labels"][0, 0].item(), 1)
+        self.assertEqual(inputs["input_boxes_labels"][1, 0].item(), 0)