🚨🚨🚨 Fix ordering of height, width for BLIP image processor (#22466)

Fix ordering of height,width for BLIP

src/transformers/models/blip/image_processing_blip.py

@@ -113,24 +113,28 @@ class BlipImageProcessor(BaseImageProcessor):
         **kwargs,
     ) -> np.ndarray:
         """
-        Resize an image.
-
-        Resizes the shorter side of the image to `size["shortest_edge"]` while preserving the aspect ratio. If the
-        longer side is larger than the max size `(int(`size["shortest_edge"]` * 1333 / 800))`, the longer side is then
-        resized to the max size while preserving the aspect ratio.
+        Resize an image to `(size["height"], size["width"])`.
 
         Args:
             image (`np.ndarray`):
                 Image to resize.
             size (`Dict[str, int]`):
-                Controls the size of the output image. Should be of the form `{"shortest_edge": int}`.
-            resample (`PILImageResampling` filter, *optional*, defaults to `PILImageResampling.BICUBIC`):
-                Resampling filter to use when resiizing the image.
-            data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format of the image. If not provided, it will be the same as the input image.
+                Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
+            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`.
+            data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the output image. If unset, the channel dimension format of the input
+                image is used. 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.
+
+        Returns:
+            `np.ndarray`: The resized image.
         """
         size = get_size_dict(size, default_to_square=True)
-        output_size = (size["width"], size["height"])
+        if "height" not in size or "width" not in size:
+            raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
+        output_size = (size["height"], size["width"])
         return resize(image, size=output_size, resample=resample, data_format=data_format, **kwargs)
 
     def rescale(