chore: import upstream snapshot with attribution

tests/transformers/test_image_processing_common.py

@@ -0,0 +1,86 @@
+# Copyright (c) 2023 PaddlePaddle Authors. 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 json
+import os
+import tempfile
+
+import numpy as np
+import paddle
+from PIL import Image
+
+from ..transformers.test_utils import check_json_file_has_correct_format
+
+
+def prepare_image_inputs(image_processor_tester, equal_resolution=False, numpify=False, paddlefy=False):
+    """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
+    or a list of PaddlePaddle tensors if one specifies paddlefy=True.
+    One can specify whether the images are of the same resolution or not.
+    """
+
+    assert not (numpify and paddlefy), "You cannot specify both numpy and PaddlePaddle tensors at the same time"
+
+    image_inputs = []
+    for i in range(image_processor_tester.batch_size):
+        if equal_resolution:
+            width = height = image_processor_tester.max_resolution
+        else:
+            # To avoid getting image width/height 0
+            min_resolution = image_processor_tester.min_resolution
+            if getattr(image_processor_tester, "size_divisor", None):
+                # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
+                min_resolution = max(image_processor_tester.size_divisor, min_resolution)
+            width, height = np.random.choice(np.arange(min_resolution, image_processor_tester.max_resolution), 2)
+        image_inputs.append(
+            np.random.randint(255, size=(image_processor_tester.num_channels, width, height), dtype=np.uint8)
+        )
+
+    if not numpify and not paddlefy:
+        # PIL expects the channel dimension as last dimension
+        image_inputs = [Image.fromarray(np.moveaxis(image, 0, -1)) for image in image_inputs]
+
+    if paddlefy:
+        image_inputs = [paddle.to_tensor(image) for image in image_inputs]
+
+    return image_inputs
+
+
+class ImageProcessingSavingTestMixin:
+    test_cast_dtype = None
+
+    def test_image_processor_to_json_string(self):
+        image_processor = self.image_processing_class(**self.image_processor_dict)
+        obj = json.loads(image_processor.to_json_string())
+        for key, value in self.image_processor_dict.items():
+            self.assertEqual(obj[key], value)
+
+    def test_image_processor_to_json_file(self):
+        image_processor_first = self.image_processing_class(**self.image_processor_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            json_file_path = os.path.join(tmpdirname, "image_processor.json")
+            image_processor_first.to_json_file(json_file_path)
+            image_processor_second = self.image_processing_class.from_json_file(json_file_path)
+
+        self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
+
+    def test_image_processor_from_and_save_pretrained(self):
+        image_processor_first = self.image_processing_class(**self.image_processor_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            saved_file = image_processor_first.save_pretrained(tmpdirname)[0]
+            check_json_file_has_correct_format(saved_file)
+            image_processor_second = self.image_processing_class.from_pretrained(tmpdirname)
+
+        self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())