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tests/models/got_ocr2/test_processing_got_ocr2.py

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+# Copyright 2024 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 GotOcr2Processor
+from transformers.testing_utils import require_vision
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+@require_vision
+class GotOcr2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = GotOcr2Processor
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer_class = cls._get_component_class_from_processor("tokenizer")
+        tokenizer = tokenizer_class.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
+        return tokenizer
+
+    @unittest.skip("GotOcr2Processor pop the image processor output 'num_patches'")
+    def test_image_processor_defaults(self):
+        pass
+
+    def test_ocr_queries(self):
+        processor = self.get_processor()
+        image_input = self.prepare_image_inputs()
+        inputs = processor(image_input, return_tensors="pt")
+        self.assertEqual(inputs["input_ids"].shape, (1, 286))
+        self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))
+
+        inputs = processor(image_input, return_tensors="pt", format=True)
+        self.assertEqual(inputs["input_ids"].shape, (1, 288))
+        self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))
+
+        inputs = processor(image_input, return_tensors="pt", color="red")
+        self.assertEqual(inputs["input_ids"].shape, (1, 290))
+        self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))
+
+        inputs = processor(image_input, return_tensors="pt", box=[0, 0, 100, 100])
+        self.assertEqual(inputs["input_ids"].shape, (1, 303))
+        self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))
+
+        inputs = processor([image_input, image_input], return_tensors="pt", multi_page=True, format=True)
+        self.assertEqual(inputs["input_ids"].shape, (1, 547))
+        self.assertEqual(inputs["pixel_values"].shape, (2, 3, 384, 384))
+
+        inputs = processor(image_input, return_tensors="pt", crop_to_patches=True, max_patches=6)
+        self.assertEqual(inputs["input_ids"].shape, (1, 1826))
+        self.assertEqual(inputs["pixel_values"].shape, (7, 3, 384, 384))
+
+    def test_processor_text_has_no_visual(self):
+        # Overwritten: requires `multi_page` kwarg to process nested vision inputs
+        processor = self.get_processor()
+
+        text = self.prepare_text_inputs(batch_size=3, modalities="image")
+        image_inputs = self.prepare_image_inputs(batch_size=3)
+        processing_kwargs = {"return_tensors": "pt", "padding": True, "multi_page": True}
+
+        # Call with nested list of vision inputs
+        image_inputs_nested = [[image] if not isinstance(image, list) else image for image in image_inputs]
+        inputs_dict_nested = {"text": text, "images": image_inputs_nested}
+        inputs = processor(**inputs_dict_nested, **processing_kwargs)
+        self.assertTrue(self.text_input_name in inputs)
+
+        # Call with one of the samples with no associated vision input
+        plain_text = "lower newer"
+        image_inputs_nested[0] = []
+        text[0] = plain_text
+        inputs_dict_no_vision = {"text": text, "images": image_inputs_nested}
+        inputs_nested = processor(**inputs_dict_no_vision, **processing_kwargs)
+        self.assertListEqual(
+            inputs[self.text_input_name][1:].tolist(), inputs_nested[self.text_input_name][1:].tolist()
+        )