Add test_image_processing_common.py (#20785)

* Add test_image_processing_common.py

* Fix typo

* Update imports and test fetcher

* Revert but keep test fetcher update

* Fix imports

* Fix all imports

* Formatting fix

* Update tests/test_image_processing_common.py

tests/models/beit/test_image_processing_beit.py

@@ -22,7 +22,8 @@ from datasets import load_dataset
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/conditional_detr/test_image_processing_conditional_detr.py

@@ -23,7 +23,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision, slow
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/convnext/test_image_processing_convnext.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/deformable_detr/test_image_processing_deformable_detr.py

@@ -23,7 +23,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision, slow
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/deit/test_image_processing_deit.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/detr/test_image_processing_detr.py

@@ -23,7 +23,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision, slow
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/donut/test_image_processing_donut.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import is_flaky, require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/dpt/test_image_processing_dpt.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.file_utils import is_torch_available, is_vision_available
 from transformers.testing_utils import require_torch, require_vision
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/efficientformer/test_image_processing_efficientformer.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/flava/test_image_processing_flava.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/glpn/test_image_processing_glpn.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/layoutlmv2/test_image_processing_layoutlmv2.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import require_pytesseract, require_torch
 from transformers.utils import is_pytesseract_available, is_torch_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/layoutlmv3/test_image_processing_layoutlmv3.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import require_pytesseract, require_torch
 from transformers.utils import is_pytesseract_available, is_torch_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/levit/test_image_processing_levit.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/maskformer/test_image_processing_maskformer.py

@@ -23,7 +23,8 @@ from huggingface_hub import hf_hub_download
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/mobilenet_v1/test_image_processing_mobilenet_v1.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/mobilenet_v2/test_image_processing_mobilenet_v2.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/mobilevit/test_image_processing_mobilevit.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/oneformer/test_image_processing_oneformer.py

@@ -23,7 +23,8 @@ from huggingface_hub import hf_hub_download
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/oneformer/test_processor_oneformer.py

@@ -26,7 +26,7 @@ from huggingface_hub import hf_hub_download
 from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import prepare_image_inputs
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/owlvit/test_image_processing_owlvit.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/poolformer/test_image_processing_poolformer.py

@@ -20,7 +20,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/segformer/test_image_processing_segformer.py

@@ -22,7 +22,8 @@ from datasets import load_dataset
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/videomae/test_image_processing_videomae.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_video_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_video_inputs
 
 
 if is_torch_available():

tests/models/vilt/test_image_processing_vilt.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/vit/test_image_processing_vit.py

@@ -21,7 +21,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/models/yolos/test_image_processing_yolos.py

@@ -23,7 +23,8 @@ import numpy as np
 from transformers.testing_utils import require_torch, require_vision, slow
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
+from ...test_image_processing_common import prepare_image_inputs
 
 
 if is_torch_available():

tests/test_feature_extraction_common.py

@@ -25,16 +25,7 @@ from pathlib import Path
 from huggingface_hub import HfFolder, delete_repo, set_access_token
 from requests.exceptions import HTTPError
 from transformers import AutoFeatureExtractor, Wav2Vec2FeatureExtractor
-from transformers.testing_utils import (
-    TOKEN,
-    USER,
-    check_json_file_has_correct_format,
-    get_tests_dir,
-    is_staging_test,
-    require_torch,
-    require_vision,
-)
-from transformers.utils import is_torch_available, is_vision_available
+from transformers.testing_utils import TOKEN, USER, check_json_file_has_correct_format, get_tests_dir, is_staging_test
 
 
 sys.path.append(str(Path(__file__).parent.parent / "utils"))
@@ -42,105 +33,9 @@ sys.path.append(str(Path(__file__).parent.parent / "utils"))
 from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
 
 
-if is_torch_available():
-    import numpy as np
-    import torch
-
-if is_vision_available():
-    from PIL import Image
-
-
 SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = get_tests_dir("fixtures")
 
 
-def prepare_image_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
-    """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
-    or a list of PyTorch tensors if one specifies torchify=True.
-
-    One can specify whether the images are of the same resolution or not.
-    """
-
-    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
-
-    image_inputs = []
-    for i in range(feature_extract_tester.batch_size):
-        if equal_resolution:
-            width = height = feature_extract_tester.max_resolution
-        else:
-            # To avoid getting image width/height 0
-            min_resolution = feature_extract_tester.min_resolution
-            if getattr(feature_extract_tester, "size_divisor", None):
-                # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
-                min_resolution = max(feature_extract_tester.size_divisor, min_resolution)
-            width, height = np.random.choice(np.arange(min_resolution, feature_extract_tester.max_resolution), 2)
-        image_inputs.append(
-            np.random.randint(
-                255,
-                size=(
-                    feature_extract_tester.num_channels,
-                    width,
-                    height,
-                ),
-                dtype=np.uint8,
-            )
-        )
-
-    if not numpify and not torchify:
-        # PIL expects the channel dimension as last dimension
-        image_inputs = [Image.fromarray(np.moveaxis(image, 0, -1)) for image in image_inputs]
-
-    if torchify:
-        image_inputs = [torch.from_numpy(image) for image in image_inputs]
-
-    return image_inputs
-
-
-def prepare_video(feature_extract_tester, width=10, height=10, numpify=False, torchify=False):
-    """This function prepares a video as a list of PIL images/NumPy arrays/PyTorch tensors."""
-
-    video = []
-    for i in range(feature_extract_tester.num_frames):
-        video.append(np.random.randint(255, size=(feature_extract_tester.num_channels, width, height), dtype=np.uint8))
-
-    if not numpify and not torchify:
-        # PIL expects the channel dimension as last dimension
-        video = [Image.fromarray(np.moveaxis(frame, 0, -1)) for frame in video]
-
-    if torchify:
-        video = [torch.from_numpy(frame) for frame in video]
-
-    return video
-
-
-def prepare_video_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
-    """This function prepares a batch of videos: a list of list of PIL images, or a list of list of numpy arrays if
-    one specifies numpify=True, or a list of list of PyTorch tensors if one specifies torchify=True.
-
-    One can specify whether the videos are of the same resolution or not.
-    """
-
-    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
-
-    video_inputs = []
-    for i in range(feature_extract_tester.batch_size):
-        if equal_resolution:
-            width = height = feature_extract_tester.max_resolution
-        else:
-            width, height = np.random.choice(
-                np.arange(feature_extract_tester.min_resolution, feature_extract_tester.max_resolution), 2
-            )
-            video = prepare_video(
-                feature_extract_tester=feature_extract_tester,
-                width=width,
-                height=height,
-                numpify=numpify,
-                torchify=torchify,
-            )
-        video_inputs.append(video)
-
-    return video_inputs
-
-
 class FeatureExtractionSavingTestMixin:
     test_cast_dtype = None
 
@@ -174,41 +69,6 @@ class FeatureExtractionSavingTestMixin:
         feat_extract = self.feature_extraction_class()
         self.assertIsNotNone(feat_extract)
 
-    @require_torch
-    @require_vision
-    def test_cast_dtype_device(self):
-        if self.test_cast_dtype is not None:
-            # Initialize feature_extractor
-            feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
-
-            # create random PyTorch tensors
-            image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
-
-            encoding = feature_extractor(image_inputs, return_tensors="pt")
-            # for layoutLM compatiblity
-            self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
-            self.assertEqual(encoding.pixel_values.dtype, torch.float32)
-
-            encoding = feature_extractor(image_inputs, return_tensors="pt").to(torch.float16)
-            self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
-            self.assertEqual(encoding.pixel_values.dtype, torch.float16)
-
-            encoding = feature_extractor(image_inputs, return_tensors="pt").to("cpu", torch.bfloat16)
-            self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
-            self.assertEqual(encoding.pixel_values.dtype, torch.bfloat16)
-
-            with self.assertRaises(TypeError):
-                _ = feature_extractor(image_inputs, return_tensors="pt").to(torch.bfloat16, "cpu")
-
-            # Try with text + image feature
-            encoding = feature_extractor(image_inputs, return_tensors="pt")
-            encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]])})
-            encoding = encoding.to(torch.float16)
-
-            self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
-            self.assertEqual(encoding.pixel_values.dtype, torch.float16)
-            self.assertEqual(encoding.input_ids.dtype, torch.long)
-
 
 class FeatureExtractorUtilTester(unittest.TestCase):
     def test_cached_files_are_used_when_internet_is_down(self):

tests/test_image_processing_common.py

@@ -0,0 +1,312 @@
+# coding=utf-8
+# Copyright 2023 HuggingFace Inc.
+#
+# 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 sys
+import tempfile
+import unittest
+import unittest.mock as mock
+from pathlib import Path
+
+from huggingface_hub import HfFolder, delete_repo, set_access_token
+from requests.exceptions import HTTPError
+from transformers import AutoImageProcessor, ViTImageProcessor
+from transformers.testing_utils import (
+    TOKEN,
+    USER,
+    check_json_file_has_correct_format,
+    get_tests_dir,
+    is_staging_test,
+    require_torch,
+    require_vision,
+)
+from transformers.utils import is_torch_available, is_vision_available
+
+
+sys.path.append(str(Path(__file__).parent.parent / "utils"))
+
+from test_module.custom_image_processing import CustomImageProcessor  # noqa E402
+
+
+if is_torch_available():
+    import numpy as np
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+
+SAMPLE_IMAGE_PROCESSING_CONFIG_DIR = get_tests_dir("fixtures")
+
+
+def prepare_image_inputs(image_processor_tester, equal_resolution=False, numpify=False, torchify=False):
+    """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
+    or a list of PyTorch tensors if one specifies torchify=True.
+
+    One can specify whether the images are of the same resolution or not.
+    """
+
+    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch 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 torchify:
+        # PIL expects the channel dimension as last dimension
+        image_inputs = [Image.fromarray(np.moveaxis(image, 0, -1)) for image in image_inputs]
+
+    if torchify:
+        image_inputs = [torch.from_numpy(image) for image in image_inputs]
+
+    return image_inputs
+
+
+def prepare_video(image_processor_tester, width=10, height=10, numpify=False, torchify=False):
+    """This function prepares a video as a list of PIL images/NumPy arrays/PyTorch tensors."""
+
+    video = []
+    for i in range(image_processor_tester.num_frames):
+        video.append(np.random.randint(255, size=(image_processor_tester.num_channels, width, height), dtype=np.uint8))
+
+    if not numpify and not torchify:
+        # PIL expects the channel dimension as last dimension
+        video = [Image.fromarray(np.moveaxis(frame, 0, -1)) for frame in video]
+
+    if torchify:
+        video = [torch.from_numpy(frame) for frame in video]
+
+    return video
+
+
+def prepare_video_inputs(image_processor_tester, equal_resolution=False, numpify=False, torchify=False):
+    """This function prepares a batch of videos: a list of list of PIL images, or a list of list of numpy arrays if
+    one specifies numpify=True, or a list of list of PyTorch tensors if one specifies torchify=True.
+
+    One can specify whether the videos are of the same resolution or not.
+    """
+
+    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
+
+    video_inputs = []
+    for i in range(image_processor_tester.batch_size):
+        if equal_resolution:
+            width = height = image_processor_tester.max_resolution
+        else:
+            width, height = np.random.choice(
+                np.arange(image_processor_tester.min_resolution, image_processor_tester.max_resolution), 2
+            )
+            video = prepare_video(
+                image_processor_tester=image_processor_tester,
+                width=width,
+                height=height,
+                numpify=numpify,
+                torchify=torchify,
+            )
+        video_inputs.append(video)
+
+    return video_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())
+
+    def test_init_without_params(self):
+        image_processor = self.image_processing_class()
+        self.assertIsNotNone(image_processor)
+
+    @require_torch
+    @require_vision
+    def test_cast_dtype_device(self):
+        if self.test_cast_dtype is not None:
+            # Initialize image_processor
+            image_processor = self.image_processing_class(**self.image_processor_dict)
+
+            # create random PyTorch tensors
+            image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
+
+            encoding = image_processor(image_inputs, return_tensors="pt")
+            # for layoutLM compatiblity
+            self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
+            self.assertEqual(encoding.pixel_values.dtype, torch.float32)
+
+            encoding = image_processor(image_inputs, return_tensors="pt").to(torch.float16)
+            self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
+            self.assertEqual(encoding.pixel_values.dtype, torch.float16)
+
+            encoding = image_processor(image_inputs, return_tensors="pt").to("cpu", torch.bfloat16)
+            self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
+            self.assertEqual(encoding.pixel_values.dtype, torch.bfloat16)
+
+            with self.assertRaises(TypeError):
+                _ = image_processor(image_inputs, return_tensors="pt").to(torch.bfloat16, "cpu")
+
+            # Try with text + image feature
+            encoding = image_processor(image_inputs, return_tensors="pt")
+            encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]])})
+            encoding = encoding.to(torch.float16)
+
+            self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
+            self.assertEqual(encoding.pixel_values.dtype, torch.float16)
+            self.assertEqual(encoding.input_ids.dtype, torch.long)
+
+
+class ImageProcessorUtilTester(unittest.TestCase):
+    def test_cached_files_are_used_when_internet_is_down(self):
+        # A mock response for an HTTP head request to emulate server down
+        response_mock = mock.Mock()
+        response_mock.status_code = 500
+        response_mock.headers = {}
+        response_mock.raise_for_status.side_effect = HTTPError
+        response_mock.json.return_value = {}
+
+        # Download this model to make sure it's in the cache.
+        _ = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
+        # Under the mock environment we get a 500 error when trying to reach the model.
+        with mock.patch("requests.request", return_value=response_mock) as mock_head:
+            _ = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
+            # This check we did call the fake head request
+            mock_head.assert_called()
+
+    def test_legacy_load_from_url(self):
+        # This test is for deprecated behavior and can be removed in v5
+        _ = ViTImageProcessor.from_pretrained(
+            "https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"
+        )
+
+
+@is_staging_test
+class ImageProcessorPushToHubTester(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls._token = TOKEN
+        set_access_token(TOKEN)
+        HfFolder.save_token(TOKEN)
+
+    @classmethod
+    def tearDownClass(cls):
+        try:
+            delete_repo(token=cls._token, repo_id="test-image-processor")
+        except HTTPError:
+            pass
+
+        try:
+            delete_repo(token=cls._token, repo_id="valid_org/test-image-processor-org")
+        except HTTPError:
+            pass
+
+        try:
+            delete_repo(token=cls._token, repo_id="test-dynamic-image-processor")
+        except HTTPError:
+            pass
+
+    def test_push_to_hub(self):
+        image_processor = ViTImageProcessor.from_pretrained(SAMPLE_IMAGE_PROCESSING_CONFIG_DIR)
+        image_processor.push_to_hub("test-image-processor", use_auth_token=self._token)
+
+        new_image_processor = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
+        for k, v in image_processor.__dict__.items():
+            self.assertEqual(v, getattr(new_image_processor, k))
+
+        # Reset repo
+        delete_repo(token=self._token, repo_id="test-image-processor")
+
+        # Push to hub via save_pretrained
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            image_processor.save_pretrained(
+                tmp_dir, repo_id="test-image-processor", push_to_hub=True, use_auth_token=self._token
+            )
+
+        new_image_processor = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
+        for k, v in image_processor.__dict__.items():
+            self.assertEqual(v, getattr(new_image_processor, k))
+
+    def test_push_to_hub_in_organization(self):
+        image_processor = ViTImageProcessor.from_pretrained(SAMPLE_IMAGE_PROCESSING_CONFIG_DIR)
+        image_processor.push_to_hub("valid_org/test-image-processor", use_auth_token=self._token)
+
+        new_image_processor = ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
+        for k, v in image_processor.__dict__.items():
+            self.assertEqual(v, getattr(new_image_processor, k))
+
+        # Reset repo
+        delete_repo(token=self._token, repo_id="valid_org/test-image-processor")
+
+        # Push to hub via save_pretrained
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            image_processor.save_pretrained(
+                tmp_dir, repo_id="valid_org/test-image-processor-org", push_to_hub=True, use_auth_token=self._token
+            )
+
+        new_image_processor = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
+        for k, v in image_processor.__dict__.items():
+            self.assertEqual(v, getattr(new_image_processor, k))
+
+    def test_push_to_hub_dynamic_image_processor(self):
+        CustomImageProcessor.register_for_auto_class()
+        image_processor = CustomImageProcessor.from_pretrained(SAMPLE_IMAGE_PROCESSING_CONFIG_DIR)
+
+        image_processor.push_to_hub("test-dynamic-image-processor", use_auth_token=self._token)
+
+        # This has added the proper auto_map field to the config
+        self.assertDictEqual(
+            image_processor.auto_map,
+            {"ImageProcessor": "custom_image_processing.CustomImageProcessor"},
+        )
+
+        new_image_processor = AutoImageProcessor.from_pretrained(
+            f"{USER}/test-dynamic-image-processor", trust_remote_code=True
+        )
+        # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
+        self.assertEqual(new_image_processor.__class__.__name__, "CustomImageProcessor")

utils/tests_fetcher.py

@@ -353,6 +353,7 @@ SPECIAL_MODULE_TO_TEST_MAP = {
     "feature_extraction_sequence_utils.py": "test_sequence_feature_extraction_common.py",
     "feature_extraction_utils.py": "test_feature_extraction_common.py",
     "file_utils.py": ["utils/test_file_utils.py", "utils/test_model_output.py"],
+    "image_processing_utils.py": ["test_image_processing_common.py", "utils/test_image_processing_utils.py"],
     "image_transforms.py": "test_image_transforms.py",
     "utils/generic.py": ["utils/test_file_utils.py", "utils/test_model_output.py", "utils/test_generic.py"],
     "utils/hub.py": "utils/test_hub_utils.py",