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tests/models/chinese_clip/test_processing_chinese_clip.py

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+# Copyright 2021 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 os
+import unittest
+
+from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
+from transformers.testing_utils import require_vision
+from transformers.utils import is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    from transformers import ChineseCLIPProcessor
+
+
+@require_vision
+class ChineseCLIPProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = ChineseCLIPProcessor
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer_class = cls._get_component_class_from_processor("tokenizer")
+        vocab_tokens = [
+            "[UNK]",
+            "[CLS]",
+            "[SEP]",
+            "[PAD]",
+            "[MASK]",
+            "的",
+            "价",
+            "格",
+            "是",
+            "15",
+            "便",
+            "alex",
+            "##andra",
+            "，",
+            "。",
+            "-",
+            "t",
+            "shirt",
+        ]
+        vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
+        with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
+            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
+        return tokenizer_class.from_pretrained(cls.tmpdirname)
+
+    @classmethod
+    def _setup_image_processor(cls):
+        image_processor_class = cls._get_component_class_from_processor("image_processor")
+        image_processor_map = {
+            "do_resize": True,
+            "size": {"height": 224, "width": 224},
+            "do_center_crop": True,
+            "crop_size": {"height": 18, "width": 18},
+            "do_normalize": True,
+            "image_mean": [0.48145466, 0.4578275, 0.40821073],
+            "image_std": [0.26862954, 0.26130258, 0.27577711],
+            "do_convert_rgb": True,
+        }
+        return image_processor_class(**image_processor_map)