add tests and more docs

src/transformers/models/donut/image_processing_donut.py

@@ -183,6 +183,7 @@ def pad_image(
             input_data_format (`ChannelDimension` or `str`, *optional*):
                 The channel dimension format of the input image. If not provided, it will be inferred.
         """
+        size = get_size_dict(size)
         output_height, output_width = size["height"], size["width"]
         input_height, input_width = get_image_size(image, channel_dim=input_data_format)
 
@@ -232,6 +233,7 @@ def thumbnail(
                 The channel dimension format of the input image. If not provided, it will be inferred.
         """
         input_height, input_width = get_image_size(image, channel_dim=input_data_format)
+        size = get_size_dict(size)
         output_height, output_width = size["height"], size["width"]
 
         # We always resize to the smallest of either the input or output size.

src/transformers/models/fuyu/image_processing_fuyu.py

@@ -19,7 +19,7 @@
 
 import numpy as np
 
-from ...image_processing_utils import BaseImageProcessor, BatchFeature
+from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
 from ...image_transforms import (
     pad,
     resize,
@@ -344,6 +344,7 @@ def pad_image(
                 The channel dimension format of the input image. If not provided, it will be inferred.
         """
         image_height, image_width = get_image_size(image, input_data_format)
+        size = get_size_dict(size)
         target_height, target_width = size["height"], size["width"]
         padding_top = 0
         padding_left = 0

src/transformers/models/nougat/image_processing_nougat.py

@@ -250,6 +250,7 @@ def pad_image(
             input_data_format (`ChannelDimension` or `str`, *optional*):
                 The channel dimension format of the input image. If not provided, it will be inferred.
         """
+        size = get_size_dict(size)
         output_height, output_width = size["height"], size["width"]
         input_height, input_width = get_image_size(image, channel_dim=input_data_format)
 
@@ -292,6 +293,7 @@ def thumbnail(
                 The channel dimension format of the input image. If not provided, it will be inferred.
         """
         input_height, input_width = get_image_size(image, channel_dim=input_data_format)
+        size = get_size_dict(size)
         output_height, output_width = size["height"], size["width"]
 
         # We always resize to the smallest of either the input or output size.

src/transformers/models/nougat/processing_nougat.py

@@ -103,7 +103,11 @@ def __call__(
             tokenizer_init_kwargs=self.tokenizer.init_kwargs,
             **kwargs,
         )
+        # Temporary fix for "paddding_side" in init_kwargs
+        _ = output_kwargs["text_kwargs"].pop("padding_side", None)
 
+        # For backwards compatibility, we reuse `audio` as `text_pair`
+        # in case downstream users passed it as a positional argument
         if output_kwargs["text_kwargs"].get("text_pair") is not None and audio is not None:
             raise ValueError(
                 "You cannot provide `text_pair` as a positional argument and as a keyword argument at the same time."
@@ -113,11 +117,11 @@ def __call__(
             warnings.warn(
                 "No `text_pair` kwarg was detected. The use of `text_pair` as an argument without specifying it explicitely as `text_pair=` will be deprecated in future versions."
             )
-            # For backwards compatibility, we reuse `audio` as `text_pair` in case
-            # downstream users passed it as a positional argument
             if audio is not None:
                 output_kwargs["text_kwargs"]["text_pair"] = audio
 
+        # For backwards compatibility, we reuse `videos` as `text_target`
+        # in case downstream users passed it as a positional argument
         if output_kwargs["text_kwargs"].get("text_target") is not None and videos is not None:
             raise ValueError(
                 "You cannot provide `text_target` as a positional argument and as a keyword argument at the same time."
@@ -127,11 +131,11 @@ def __call__(
             warnings.warn(
                 "No `text_target` kwarg was detected. The use of `text_target` as an argument without specifying it explicitely as `text_target=` will be deprecated in future versions."
             )
-            # For backwards compatibility, we reuse `videos` as `text_target` in case
-            # downstream users passed it as a positional argument
             if videos is not None:
                 output_kwargs["text_kwargs"]["text_target"] = videos
 
+        # For backwards compatibility, we reuse `backwards_compatibility_placeholder_arg` as `text_pair_target`
+        # in case downstream users passed it as a positional argument
         if (
             output_kwargs["text_kwargs"].get("text_pair_target") is not None
             and backwards_compatibility_placeholder_arg is not None
@@ -144,8 +148,6 @@ def __call__(
             warnings.warn(
                 "No `text_pair_target` kwarg was detected. The use of `text_pair_target` as an argument without specifying it explicitely as `text_pair_target=` will be deprecated in future versions."
             )
-            # For backwards compatibility, we reuse `backwards_compatibility_placeholder_arg` as `text_pair_target` in case
-            # downstream users passed it as a positional argument
             if backwards_compatibility_placeholder_arg is not None:
                 output_kwargs["text_kwargs"]["text_pair_target"] = backwards_compatibility_placeholder_arg
 

src/transformers/models/sam/image_processing_sam.py

@@ -185,6 +185,7 @@ def pad_image(
             input_data_format (`str` or `ChannelDimension`, *optional*):
                 The channel dimension format of the input image. If not provided, it will be inferred.
         """
+        pad_size = get_size_dict(pad_size)
         output_height, output_width = pad_size["height"], pad_size["width"]
         input_height, input_width = get_image_size(image, channel_dim=input_data_format)
 

src/transformers/models/tvp/image_processing_tvp.py

@@ -244,6 +244,7 @@ def pad_image(
                 The channel dimension format of the input image. If not provided, it will be inferred.
         """
         height, width = get_image_size(image, channel_dim=input_data_format)
+        pad_size = get_size_dict(pad_size)
         max_height = pad_size.get("height", height)
         max_width = pad_size.get("width", width)
 

tests/models/nougat/test_processor_nougat.py

@@ -0,0 +1,17 @@
+import tempfile
+import unittest
+
+from transformers import NougatProcessor
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+class NougatProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    from_pretrained_id = "facebook/nougat-base"
+    text_data_arg_name = "labels"
+    processor_class = NougatProcessor
+
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+        processor = self.processor_class.from_pretrained(self.from_pretrained_id)
+        processor.save_pretrained(self.tmpdirname)

tests/test_processing_common.py

@@ -48,6 +48,8 @@
 @require_vision
 @require_torch
 class ProcessorTesterMixin:
+    image_data_arg_name = "pixel_values"
+    text_data_arg_name = "input_ids"
     processor_class = None
 
     def prepare_processor_dict(self):
@@ -136,7 +138,7 @@ def test_tokenizer_defaults_preserved_by_kwargs(self):
         image_input = self.prepare_image_inputs()
 
         inputs = processor(text=input_str, images=image_input, return_tensors="pt")
-        self.assertEqual(len(inputs["input_ids"][0]), 117)
+        self.assertEqual(len(inputs[self.text_data_arg_name][0]), 117)
 
     @require_torch
     @require_vision
@@ -153,7 +155,7 @@ def test_image_processor_defaults_preserved_by_image_kwargs(self):
         image_input = self.prepare_image_inputs()
 
         inputs = processor(text=input_str, images=image_input)
-        self.assertEqual(len(inputs["pixel_values"][0][0]), 234)
+        self.assertEqual(len(inputs[self.image_data_arg_name][0][0]), 234)
 
     @require_vision
     @require_torch
@@ -171,7 +173,7 @@ def test_kwargs_overrides_default_tokenizer_kwargs(self):
         inputs = processor(
             text=input_str, images=image_input, return_tensors="pt", max_length=112, padding="max_length"
         )
-        self.assertEqual(len(inputs["input_ids"][0]), 112)
+        self.assertEqual(len(inputs[self.text_data_arg_name][0]), 112)
 
     @require_torch
     @require_vision
@@ -188,7 +190,7 @@ def test_kwargs_overrides_default_image_processor_kwargs(self):
         image_input = self.prepare_image_inputs()
 
         inputs = processor(text=input_str, images=image_input, size=[224, 224])
-        self.assertEqual(len(inputs["pixel_values"][0][0]), 224)
+        self.assertEqual(len(inputs[self.image_data_arg_name][0][0]), 224)
 
     @require_torch
     @require_vision
@@ -212,8 +214,8 @@ def test_unstructured_kwargs(self):
             max_length=76,
         )
 
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
-        self.assertEqual(len(inputs["input_ids"][0]), 76)
+        self.assertEqual(inputs[self.image_data_arg_name].shape[2], 214)
+        self.assertEqual(len(inputs[self.text_data_arg_name][0]), 76)
 
     @require_torch
     @require_vision
@@ -237,9 +239,9 @@ def test_unstructured_kwargs_batched(self):
             max_length=76,
         )
 
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
+        self.assertEqual(inputs[self.image_data_arg_name].shape[2], 214)
 
-        self.assertEqual(len(inputs["input_ids"][0]), 6)
+        self.assertEqual(len(inputs[self.text_data_arg_name][0]), 6)
 
     @require_torch
     @require_vision
@@ -286,9 +288,9 @@ def test_structured_kwargs_nested(self):
         inputs = processor(text=input_str, images=image_input, **all_kwargs)
         self.skip_processor_without_typed_kwargs(processor)
 
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
+        self.assertEqual(inputs[self.image_data_arg_name].shape[2], 214)
 
-        self.assertEqual(len(inputs["input_ids"][0]), 76)
+        self.assertEqual(len(inputs[self.text_data_arg_name][0]), 76)
 
     @require_torch
     @require_vision
@@ -312,9 +314,9 @@ def test_structured_kwargs_nested_from_dict(self):
         }
 
         inputs = processor(text=input_str, images=image_input, **all_kwargs)
-        self.assertEqual(inputs["pixel_values"].shape[2], 214)
+        self.assertEqual(inputs[self.image_data_arg_name].shape[2], 214)
 
-        self.assertEqual(len(inputs["input_ids"][0]), 76)
+        self.assertEqual(len(inputs[self.text_data_arg_name][0]), 76)
 
 
 class MyProcessor(ProcessorMixin):