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tests/models/pp_chart2table/test_processing_pp_chart2table.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
+
+from transformers import PPChart2TableProcessor
+from transformers.testing_utils import require_vision
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+@require_vision
+class PPChart2TableProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = PPChart2TableProcessor
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer_class = cls._get_component_class_from_processor("tokenizer")
+        tokenizer = tokenizer_class.from_pretrained("PaddlePaddle/PP-Chart2Table_safetensors")
+        return tokenizer
+
+    def test_ocr_queries(self):
+        processor = self.get_processor()
+        image_input = self.prepare_image_inputs()
+        conversation = [{"role": "user", "content": []}]
+        inputs = processor.apply_chat_template(
+            conversation,
+            tokenize=False,
+            add_generation_prompt=True,
+        )
+        inputs = processor(images=image_input, text=inputs, return_tensors="pt")
+        self.assertEqual(inputs["input_ids"].shape, (1, 286))
+        self.assertEqual(inputs["pixel_values"].shape, (1, 3, 1024, 1024))
+
+    def test_unstructured_kwargs_batched(self):
+        if "image_processor" not in self.processor_class.get_attributes():
+            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
+        processor_components = self.prepare_components()
+        processor_kwargs = self.prepare_processor_dict()
+        processor = self.processor_class(**processor_components, **processor_kwargs)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = self.prepare_text_inputs(batch_size=2, modalities="image")
+        image_input = self.prepare_image_inputs(batch_size=2)
+        inputs = processor(
+            text=input_str,
+            images=image_input,
+            return_tensors="pt",
+            do_rescale=True,
+            rescale_factor=-1.0,
+            padding="longest",
+            max_length=self.image_unstructured_max_length,
+        )
+
+        self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0)
+
+    @unittest.skip(
+        reason="PPChart2Table relies on a heavily predetermined input format; chat template usage is not intended as expected"
+    )
+    def test_apply_chat_template_assistant_mask(self):
+        pass
+
+    @unittest.skip(
+        reason="PPChart2Table relies on a heavily predetermined input format; chat template usage is not intended as expected"
+    )
+    def test_apply_chat_template_image_0(self):
+        pass
+
+    @unittest.skip(
+        reason="PPChart2Table relies on a heavily predetermined input format; chat template usage is not intended as expected"
+    )
+    def test_apply_chat_template_image_1(self):
+        pass