chore: import upstream snapshot with attribution

examples/modular-transformers/image_processing_new_imgproc_model.py

@@ -0,0 +1,27 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from examples/modular-transformers/modular_new_imgproc_model.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_new_imgproc_model.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+import torch
+
+from ...image_processing_backends import TorchvisionBackend
+from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, PILImageResampling
+from ...utils import auto_docstring
+
+
+@auto_docstring
+class ImgprocModelImageProcessor(TorchvisionBackend):
+    resample = PILImageResampling.BICUBIC
+    image_mean = OPENAI_CLIP_MEAN
+    image_std = OPENAI_CLIP_STD
+    size = {"height": 384, "width": 384}
+    default_to_square = True
+    do_resize = True
+    do_rescale = True
+    do_normalize = True
+    do_convert_rgb = True
+
+    def new_image_processing_method(self, pixel_values: torch.FloatTensor):
+        return pixel_values / 2