Fix code examples

docs/source/en/model_doc/donut.mdx

@@ -47,20 +47,69 @@ The [`DonutFeatureExtractor`] class is responsible for preprocessing the input i
 [`DonutProcessor`] wraps [`DonutFeatureExtractor`] and [`XLMRobertaTokenizer`/`XLMRobertaTokenizerFast`]
 into a single instance to both extract the input features and decode the predicted token ids.
 
+- Step-by-step Document Image Classification
+
+```py
+>>> import re
+
+>>> from transformers import DonutProcessor, VisionEncoderDecoderModel
+>>> from datasets import load_dataset
+>>> import torch
+
+>>> processor = DonutProcessor.from_pretrained("nielsr/donut-base-finetuned-rvlcdip")
+>>> model = VisionEncoderDecoderModel.from_pretrained("nielsr/donut-base-finetuned-rvlcdip")
+
+>>> device = "cuda" if torch.cuda.is_available() else "cpu"
+>>> model.to(device)  # doctest: +IGNORE_RESULT
+
+>>> # load document image
+>>> dataset = load_dataset("hf-internal-testing/example-documents", split="test")
+>>> image = dataset[1]["image"]
+
+>>> # prepare decoder inputs
+>>> task_prompt = "<s_rvlcdip>"
+>>> decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
+
+>>> pixel_values = processor(image, return_tensors="pt").pixel_values
+
+>>> outputs = model.generate(
+...     pixel_values.to(device),
+...     decoder_input_ids=decoder_input_ids.to(device),
+...     max_length=model.decoder.config.max_position_embeddings,
+...     early_stopping=True,
+...     pad_token_id=processor.tokenizer.pad_token_id,
+...     eos_token_id=processor.tokenizer.eos_token_id,
+...     use_cache=True,
+...     num_beams=1,
+...     bad_words_ids=[[processor.tokenizer.unk_token_id]],
+...     return_dict_in_generate=True,
+... )
+
+>>> sequence = processor.batch_decode(outputs.sequences)[0]
+>>> sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
+>>> sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove first task start token
+>>> print(sequence)
+<s_class><advertisement/></s_class>
+```
+
+We refer to the example notebooks regarding converting the model output back to JSON. 
+The code is exactly the same for document parsing, except that the task prompt is different (e.g. "<s_cord-v2>").
+Another example can be found below:
+
 - Step-by-step Document Visual Question Answering (DocVQA)
 
-``` py
+```py
 >>> import re
 
 >>> from transformers import DonutProcessor, VisionEncoderDecoderModel
 >>> from datasets import load_dataset
 >>> import torch
 
->>> processor = DonutProcessor.from_pretrained("nielsr/donut-base-finetuned-docvqa") 
+>>> processor = DonutProcessor.from_pretrained("nielsr/donut-base-finetuned-docvqa")
 >>> model = VisionEncoderDecoderModel.from_pretrained("nielsr/donut-base-finetuned-docvqa")
 
 >>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model.to(device)
+>>> model.to(device)  # doctest: +IGNORE_RESULT
 
 >>> # load document image from the DocVQA dataset
 >>> dataset = load_dataset("hf-internal-testing/example-documents", split="test")
@@ -72,18 +121,20 @@ into a single instance to both extract the input features and decode the predict
 >>> prompt = task_prompt.replace("{user_input}", question)
 >>> decoder_input_ids = processor.tokenizer(prompt, add_special_tokens=False, return_tensors="pt").input_ids
 
->>> pixel_values = processor(image, return_tensors="pt").pixel_values 
-
->>> outputs = model.generate(pixel_values.to(device),
-...                          decoder_input_ids=decoder_input_ids.to(device),
-...                          max_length=model.decoder.config.max_position_embeddings,
-...                          early_stopping=True,
-...                          pad_token_id=processor.tokenizer.pad_token_id,
-...                          eos_token_id=processor.tokenizer.eos_token_id,
-...                          use_cache=True,
-...                          num_beams=1,
-...                          bad_words_ids=[[processor.tokenizer.unk_token_id]],
-...                          return_dict_in_generate=True)
+>>> pixel_values = processor(image, return_tensors="pt").pixel_values
+
+>>> outputs = model.generate(
+...     pixel_values.to(device),
+...     decoder_input_ids=decoder_input_ids.to(device),
+...     max_length=model.decoder.config.max_position_embeddings,
+...     early_stopping=True,
+...     pad_token_id=processor.tokenizer.pad_token_id,
+...     eos_token_id=processor.tokenizer.eos_token_id,
+...     use_cache=True,
+...     num_beams=1,
+...     bad_words_ids=[[processor.tokenizer.unk_token_id]],
+...     return_dict_in_generate=True,
+... )
 
 >>> sequence = processor.batch_decode(outputs.sequences)[0]
 >>> sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")