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tests/models/pi0/test_processing_pi0.py

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+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# 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
+
+import torch
+
+from transformers import PI0Processor
+from transformers.testing_utils import get_tests_dir, require_torch, require_vision
+from transformers.utils import is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
+
+
+if is_vision_available():
+    from transformers import GemmaTokenizer, SiglipImageProcessor
+
+
+@require_vision
+class PI0ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = PI0Processor
+
+    @classmethod
+    def _setup_image_processor(cls):
+        image_processor = SiglipImageProcessor.from_pretrained("google/siglip-so400m-patch14-384")
+        image_processor.image_seq_length = 0
+        return image_processor
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer = GemmaTokenizer.from_pretrained(SAMPLE_VOCAB, keep_accents=True)
+        tokenizer.add_special_tokens({"additional_special_tokens": ["<image>"]})
+        return tokenizer
+
+    @classmethod
+    def _setup_test_attributes(cls, processor):
+        cls.image_token = processor.image_token
+
+    @require_vision
+    def prepare_image_inputs(self, batch_size: int | None = None, nested: bool = True):
+        return super().prepare_image_inputs(batch_size, nested=nested)
+
+    def test_image_processor_defaults(self):
+        image_processor = self.get_component("image_processor")
+        processor = self.get_processor()
+        image_input = self.prepare_image_inputs()
+
+        input_image_proc = image_processor(image_input, return_tensors="pt")
+        input_processor = processor(images=image_input, text="", return_tensors="pt")
+
+        for key in input_image_proc:
+            torch.testing.assert_close(input_image_proc[key], input_processor[key][:, 0])
+        self.assertTrue(torch.equal(input_processor["pixel_attention_mask"], torch.tensor([[True]])))
+
+    @require_torch
+    def test_single_camera_output_is_5d(self):
+        processor = self.get_processor()
+        image = self.prepare_image_inputs()
+        outputs = processor(images=image, text="task", return_tensors="pt")
+        self.assertEqual(outputs["pixel_values"].ndim, 5)
+        self.assertEqual(outputs["pixel_values"].shape[0], 1)
+        self.assertEqual(outputs["pixel_values"].shape[1], 1)
+        self.assertTrue(torch.equal(outputs["pixel_attention_mask"], torch.tensor([[True]])))
+
+    @require_torch
+    def test_multi_camera_padding_and_masks(self):
+        processor = self.get_processor()
+        image_a = self.prepare_image_inputs()
+        image_b = self.prepare_image_inputs()
+        image_c = self.prepare_image_inputs()
+
+        outputs = processor(
+            images=[[image_a, image_b], [image_c]],
+            text=["task a", "task b"],
+            return_tensors="pt",
+        )
+
+        self.assertEqual(outputs["pixel_values"].ndim, 5)
+        self.assertEqual(outputs["pixel_values"].shape[:2], torch.Size([2, 2]))
+        self.assertTrue(torch.equal(outputs["pixel_attention_mask"], torch.tensor([[True, True], [True, False]])))
+
+    @require_torch
+    def test_newline_normalization(self):
+        processor = self.get_processor()
+        image = self.prepare_image_inputs()
+        out_no_newline = processor(images=image, text="pick object", return_tensors="pt")
+        out_with_newline = processor(images=image, text="pick object\n", return_tensors="pt")
+        self.assertTrue(torch.equal(out_no_newline["input_ids"], out_with_newline["input_ids"]))
+        self.assertTrue(torch.equal(out_no_newline["attention_mask"], out_with_newline["attention_mask"]))
+
+    @unittest.skip("PI0 doesn't need vLLM integration")
+    def test_get_num_multimodal_tokens_matches_processor_call(self):
+        pass