support the trocr small models

docs/source/model_doc/trocr.mdx

@@ -55,9 +55,9 @@ Tips:
 TrOCR's [`VisionEncoderDecoder`] model accepts images as input and makes use of
 [`~generation_utils.GenerationMixin.generate`] to autoregressively generate text given the input image.
 
-The [`ViTFeatureExtractor`] class is responsible for preprocessing the input image and
-[`RobertaTokenizer`] decodes the generated target tokens to the target string. The
-[`TrOCRProcessor`] wraps [`ViTFeatureExtractor`] and [`RobertaTokenizer`]
+The [`ViTFeatureExtractor`/`DeiTFeatureExtractor`] class is responsible for preprocessing the input image and
+[`RobertaTokenizer`/`XLMRobertaTokenizer`] decodes the generated target tokens to the target string. The
+[`TrOCRProcessor`] wraps [`ViTFeatureExtractor`/`DeiTFeatureExtractor`] and [`RobertaTokenizer`/`XLMRobertaTokenizer`]
 into a single instance to both extract the input features and decode the predicted token ids.
 
 - Step-by-step Optical Character Recognition (OCR)

src/transformers/models/trocr/processing_trocr.py

@@ -20,32 +20,34 @@
 from transformers.feature_extraction_utils import FeatureExtractionMixin
 from transformers.models.roberta.tokenization_roberta import RobertaTokenizer
 from transformers.models.roberta.tokenization_roberta_fast import RobertaTokenizerFast
+from transformers.models.xlm_roberta.tokenization_xlm_roberta import XLMRobertaTokenizer
+from transformers.models.xlm_roberta.tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
 
-from ..auto.feature_extraction_auto import AutoFeatureExtractor
+from transformers import AutoTokenizer, AutoFeatureExtractor
 
 
 class TrOCRProcessor:
     r"""
     Constructs a TrOCR processor which wraps a vision feature extractor and a TrOCR tokenizer into a single processor.
 
-    [`TrOCRProcessor`] offers all the functionalities of [`AutoFeatureExtractor`] and [`RobertaTokenizer`]. See the
+    [`TrOCRProcessor`] offers all the functionalities of [`ViTFeatureExtractor`/`DeiTFeatureExtractor`] and [`RobertaTokenizer`/`XLMRobertaTokenizer`]. See the
     [`~TrOCRProcessor.__call__`] and [`~TrOCRProcessor.decode`] for more information.
 
     Args:
-        feature_extractor ([`AutoFeatureExtractor`]):
-            An instance of [`AutoFeatureExtractor`]. The feature extractor is a required input.
-        tokenizer ([`RobertaTokenizer`]):
-            An instance of [`RobertaTokenizer`]. The tokenizer is a required input.
+        feature_extractor ([`ViTFeatureExtractor`/`DeiTFeatureExtractor`]):
+            An instance of [`ViTFeatureExtractor`/`DeiTFeatureExtractor`]. The feature extractor is a required input.
+        tokenizer ([`RobertaTokenizer`/`XLMRobertaTokenizer`]):
+            An instance of [`RobertaTokenizer`/`XLMRobertaTokenizer`]. The tokenizer is a required input.
     """
 
     def __init__(self, feature_extractor, tokenizer):
         if not isinstance(feature_extractor, FeatureExtractionMixin):
             raise ValueError(
                 f"`feature_extractor` has to be of type {FeatureExtractionMixin.__class__}, but is {type(feature_extractor)}"
             )
-        if not (isinstance(tokenizer, RobertaTokenizer) or (isinstance(tokenizer, RobertaTokenizerFast))):
+        if not isinstance(tokenizer, (RobertaTokenizer, RobertaTokenizerFast, XLMRobertaTokenizer, XLMRobertaTokenizerFast)):
             raise ValueError(
-                f"`tokenizer` has to be of type {RobertaTokenizer.__class__} or {RobertaTokenizerFast.__class__}, but is {type(tokenizer)}"
+                f"`tokenizer` has to be of type {RobertaTokenizer.__class__} or {RobertaTokenizerFast.__class__} or {XLMRobertaTokenizer.__class__} or {XLMRobertaTokenizerFast.__class__}, but is {type(tokenizer)}"
             )
 
         self.feature_extractor = feature_extractor
@@ -103,7 +105,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
                 [`PreTrainedTokenizer`]
         """
         feature_extractor = AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        tokenizer = RobertaTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
 
         return cls(feature_extractor=feature_extractor, tokenizer=tokenizer)