Tidy up

src/transformers/image_processing_utils_fast.py

@@ -13,7 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from functools import cache
+import functools
 
 from .image_processing_utils import BaseImageProcessor
 
@@ -42,16 +42,14 @@ def _build_transforms(self, **kwargs):
         raise NotImplementedError
 
     def set_transforms(self, **kwargs):
-        # FIXME - put input validation or kwargs for all these methods
-
         if self._same_transforms_settings(**kwargs):
             return self._transforms
 
         transforms = self._build_transforms(**kwargs)
         self._set_transform_settings(**kwargs)
         self._transforms = transforms
 
-    @cache
+    @functools.lru_cache(maxsize=1)
     def _maybe_update_transforms(self, **kwargs):
         if self._same_transforms_settings(**kwargs):
             return

src/transformers/models/vit/image_processing_vit_fast.py

@@ -119,7 +119,7 @@ def __init__(
         size: Optional[Dict[str, int]] = None,
         resample: PILImageResampling = PILImageResampling.BILINEAR,
         do_rescale: bool = True,
-        rescale_factor: Union[int, float] = 1 / 255,
+        rescale_factor: Union[int, float] = None,
         do_normalize: bool = True,
         image_mean: Optional[Union[float, List[float]]] = None,
         image_std: Optional[Union[float, List[float]]] = None,
@@ -190,12 +190,15 @@ def _validate_input_arguments(
         if data_format != ChannelDimension.FIRST:
             raise ValueError("Only channel first data format is currently supported.")
 
-        if do_resize and size is None:
-            raise ValueError("Size must be specified if do_resize is True.")
+        if do_resize and None in (size, resample):
+            raise ValueError("Size and resample must be specified if do_resize is True.")
 
         if do_rescale and rescale_factor is None:
             raise ValueError("Rescale factor must be specified if do_rescale is True.")
 
+        if do_normalize and None in (image_mean, image_std):
+            raise ValueError("Image mean and standard deviation must be specified if do_normalize is True.")
+
     def preprocess(
         self,
         images: ImageInput,
@@ -238,24 +241,26 @@ def preprocess(
             image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
                 Image standard deviation to use if `do_normalize` is set to `True`.
             return_tensors (`str` or `TensorType`, *optional*):
-                The type of tensors to return. Can be one of:
-                - Unset: Return a list of `np.ndarray`.
-                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
-                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
-                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
-                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+                The type of tensors to return. Only "pt" is supported
             data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
-                The channel dimension format for the output image. Can be one of:
+                The channel dimension format for the output image. The following formats are currently supported:
                 - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-                - Unset: Use the channel dimension format of the input image.
             input_data_format (`ChannelDimension` or `str`, *optional*):
                 The channel dimension format for the input image. If unset, the channel dimension format is inferred
                 from the input image. Can be one of:
                 - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                 - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                 - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
         """
+        if return_tensors != "pt":
+            raise ValueError("Only returning PyTorch tensors is currently supported.")
+
+        if input_data_format is not None and input_data_format != ChannelDimension.FIRST:
+            raise ValueError("Only channel first data format is currently supported.")
+
+        if data_format != ChannelDimension.FIRST:
+            raise ValueError("Only channel first data format is currently supported.")
+
         do_resize = do_resize if do_resize is not None else self.do_resize
         do_rescale = do_rescale if do_rescale is not None else self.do_rescale
         do_normalize = do_normalize if do_normalize is not None else self.do_normalize