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transformers/tests/models/chameleon/test_processing_chameleon.py

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+# Copyright 2024 The HuggingFace Inc. 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.
+"""Testing suite for the PyTorch chameleon model."""
+
+import tempfile
+import unittest
+
+from transformers import ChameleonProcessor, LlamaTokenizer
+from transformers.testing_utils import get_tests_dir
+from transformers.utils import is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    from transformers import ChameleonImageProcessor
+
+
+SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
+
+
+class ChameleonProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = ChameleonProcessor
+
+    @classmethod
+    def setUpClass(cls):
+        cls.tmpdirname = tempfile.mkdtemp()
+        image_processor = ChameleonImageProcessor()
+        tokenizer = LlamaTokenizer(vocab_file=SAMPLE_VOCAB)
+        tokenizer.pad_token_id = 0
+        tokenizer.sep_token_id = 1
+        tokenizer.add_special_tokens({"additional_special_tokens": ["<image>"]})
+        processor = cls.processor_class(image_processor=image_processor, tokenizer=tokenizer, image_seq_length=2)
+        processor.save_pretrained(cls.tmpdirname)
+        cls.image_token = processor.image_token
+
+    def test_special_mm_token_truncation(self):
+        """Tests that special vision tokens do not get truncated when `truncation=True` is set."""
+
+        processor = self.get_processor()
+
+        input_str = self.prepare_text_inputs(batch_size=2, modalities="image")
+        image_input = self.prepare_image_inputs(batch_size=2)
+
+        _ = processor(
+            text=input_str,
+            images=image_input,
+            return_tensors="pt",
+            truncation=None,
+            padding=True,
+        )
+
+        with self.assertRaises(ValueError):
+            _ = processor(
+                text=input_str,
+                images=image_input,
+                return_tensors="pt",
+                truncation=True,
+                padding=True,
+                max_length=20,
+            )
+
+    @staticmethod
+    def prepare_processor_dict():
+        return {"image_seq_length": 2}  # fmt: skip
+
+    # Copied from tests.models.llava.test_processing_llava.LlavaProcessorTest.test_get_num_vision_tokens
+    def test_get_num_vision_tokens(self):
+        "Tests general functionality of the helper used internally in vLLM"
+
+        processor = self.get_processor()
+
+        output = processor._get_num_multimodal_tokens(image_sizes=[(100, 100), (300, 100), (500, 30)])
+        self.assertTrue("num_image_tokens" in output)
+        self.assertEqual(len(output["num_image_tokens"]), 3)
+
+        self.assertTrue("num_image_patches" in output)
+        self.assertEqual(len(output["num_image_patches"]), 3)