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tests/models/deepseek_ocr2/test_processing_deepseek_ocr2.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 DeepseekOcr2Processor
+from transformers.testing_utils import require_vision
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+@require_vision
+class DeepseekOcr2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = DeepseekOcr2Processor
+
+    @classmethod
+    def _setup_image_processor(cls):
+        image_processor_class = cls._get_component_class_from_processor("image_processor")
+        image_processor = image_processor_class()
+        image_processor.size = {"height": 64, "width": 64}
+        image_processor.tile_size = 512
+        return image_processor
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer_class = cls._get_component_class_from_processor("tokenizer")
+        tokenizer = tokenizer_class.from_pretrained("deepseek-community/DeepSeek-OCR-2")
+        return tokenizer
+
+    @classmethod
+    def _setup_test_attributes(cls, processor):
+        cls.image_token = processor.image_token
+
+    @unittest.skip("DeepseekOcr2Processor pops the image processor output 'num_local_patches'")
+    def test_image_processor_defaults(self):
+        pass
+
+    def test_image_token_expansion_small_image(self):
+        """Small image (< tile_size) should produce no local patches → 257 image tokens."""
+        processor = self.get_processor()
+        processor.image_processor.size = {"height": 1024, "width": 1024}
+        processor.image_processor.tile_size = 768
+
+        # Small image: max(200, 300) < 768 → no local patches
+        image = torch.randint(0, 256, (3, 300, 200), dtype=torch.uint8)
+        prompt = "<image>\nFree OCR."
+
+        inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+        image_token_id = processor.image_token_id
+        num_image_tokens = (inputs["input_ids"] == image_token_id).sum().item()
+
+        # 257 = 256 global + 0 local + 1 separator
+        self.assertEqual(num_image_tokens, 257)
+        self.assertNotIn("pixel_values_local", inputs)
+
+    def test_image_token_expansion_large_image(self):
+        """Large image should produce local patches → more image tokens."""
+        processor = self.get_processor()
+        processor.image_processor.size = {"height": 1024, "width": 1024}
+        processor.image_processor.tile_size = 768
+
+        # Large image: max(2448, 3264) > 768 → local patches
+        image = torch.randint(0, 256, (3, 3264, 2448), dtype=torch.uint8)
+        prompt = "<image>\nFree OCR."
+
+        inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+        image_token_id = processor.image_token_id
+        num_image_tokens = (inputs["input_ids"] == image_token_id).sum().item()
+        num_local_patches = inputs["num_local_patches"][0]
+
+        # 3264x2448 image produces 6 local patches (2x3 grid) + 1 global view = 7 total
+        # num_image_tokens = 256 global + 144*6 local + 1 separator = 1121
+        self.assertEqual(num_local_patches, 6)
+        self.assertEqual(num_image_tokens, 1121)
+        self.assertIn("pixel_values_local", inputs)