Add code example for document parsing

docs/source/en/model_doc/donut.mdx

@@ -92,9 +92,50 @@ into a single instance to both extract the input features and decode the predict
 {'class': 'advertisement'}
 ```
 
-The code is exactly the same for document parsing, except that the task prompt is different (e.g. "<s_cord-v2>").
+- Step-by-step Document Parsing
 
-Another example can be found below:
+```py
+>>> import re
+
+>>> from transformers import DonutProcessor, VisionEncoderDecoderModel
+>>> from datasets import load_dataset
+>>> import torch
+
+>>> processor = DonutProcessor.from_pretrained("nielsr/donut-base-finetuned-cord-v2")
+>>> model = VisionEncoderDecoderModel.from_pretrained("nielsr/donut-base-finetuned-cord-v2")
+
+>>> 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[2]["image"]
+
+>>> # prepare decoder inputs
+>>> task_prompt = "<s_cord-v2>"
+>>> 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(processor.token2json(sequence))
+{'menu': {'nm': 'CINNAMON SUGAR', 'unitprice': '17,000', 'cnt': '1 x', 'price': '17,000'}, 'sub_total': {'subtotal_price': '17,000'}, 'total': {'total_price': '17,000', 'cashprice': '20,000', 'changeprice': '3,000'}}
+```
 
 - Step-by-step Document Visual Question Answering (DocVQA)