[DocTests] Fix some doc tests (#16889)

* [DocTests] Fix some doc tests

* hacky fix

* correct

docs/source/en/model_doc/t5.mdx

@@ -252,10 +252,9 @@ The example above only shows a single example. You can also do batched inference
 >>> model = T5ForConditionalGeneration.from_pretrained("t5-small")
 
 >>> task_prefix = "translate English to German: "
->>> sentences = [
-...     "The house is wonderful.",
-...     "I like to work in NYC.",
->>> ]  # use different length sentences to test batching
+>>> # use different length sentences to test batching
+>>> sentences = ["The house is wonderful.", "I like to work in NYC."]
+
 >>> inputs = tokenizer([task_prefix + sentence for sentence in sentences], return_tensors="pt", padding=True)
 
 >>> output_sequences = model.generate(

src/transformers/models/beit/modeling_beit.py

@@ -1210,14 +1210,14 @@ class BeitForSemanticSegmentation(BeitPreTrainedModel):
         Examples:
 
         ```python
-        >>> from transformers import BeitFeatureExtractor, BeitForSemanticSegmentation
+        >>> from transformers import AutoFeatureExtractor, BeitForSemanticSegmentation
         >>> from PIL import Image
         >>> import requests
 
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
-        >>> feature_extractor = BeitFeatureExtractor.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
+        >>> feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
         >>> model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
 
         >>> inputs = feature_extractor(images=image, return_tensors="pt")

src/transformers/models/data2vec/modeling_data2vec_vision.py

@@ -1140,14 +1140,14 @@ class Data2VecVisionForSemanticSegmentation(Data2VecVisionPreTrainedModel):
         Examples:
 
         ```python
-        >>> from transformers import Data2VecVisionFeatureExtractor, Data2VecVisionForSemanticSegmentation
+        >>> from transformers import AutoFeatureExtractor, Data2VecVisionForSemanticSegmentation
         >>> from PIL import Image
         >>> import requests
 
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
-        >>> feature_extractor = Data2VecVisionFeatureExtractor.from_pretrained("facebook/data2vec-vision-base")
+        >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/data2vec-vision-base")
         >>> model = Data2VecVisionForSemanticSegmentation.from_pretrained("facebook/data2vec-vision-base")
 
         >>> inputs = feature_extractor(images=image, return_tensors="pt")