More fixes for doctest (#30265)

* fix

* update

* update

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

docs/source/en/model_doc/code_llama.md

@@ -97,6 +97,7 @@ If you only want the infilled part:
 
 >>> generator = pipeline("text-generation",model="codellama/CodeLlama-7b-hf",torch_dtype=torch.float16, device_map="auto")
 >>> generator('def remove_non_ascii(s: str) -> str:\n    """ <FILL_ME>\n    return result', max_new_tokens = 128)
+[{'generated_text': 'def remove_non_ascii(s: str) -> str:\n    """ <FILL_ME>\n    return resultRemove non-ASCII characters from a string. """\n    result = ""\n    for c in s:\n        if ord(c) < 128:\n            result += c'}]
 ```
 
 Under the hood, the tokenizer [automatically splits by `<FILL_ME>`](https://huggingface.co/docs/transformers/main/model_doc/code_llama#transformers.CodeLlamaTokenizer.fill_token) to create a formatted input string that follows [the original training pattern](https://github.com/facebookresearch/codellama/blob/cb51c14ec761370ba2e2bc351374a79265d0465e/llama/generation.py#L402). This is more robust than preparing the pattern yourself: it avoids pitfalls, such as token glueing, that are very hard to debug.  To see how much CPU and GPU memory you need for this model or others, try [this calculator](https://huggingface.co/spaces/hf-accelerate/model-memory-usage) which can help determine that value.

docs/source/en/pipeline_tutorial.md

@@ -270,11 +270,13 @@ For example, if you use this [invoice image](https://huggingface.co/spaces/impir
 >>> from transformers import pipeline
 
 >>> vqa = pipeline(model="impira/layoutlm-document-qa")
->>> vqa(
+>>> output = vqa(
 ...     image="https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png",
 ...     question="What is the invoice number?",
 ... )
-[{'score': 0.42515, 'answer': 'us-001', 'start': 16, 'end': 16}]
+>>> output[0]["score"] = round(output[0]["score"], 3)
+>>> output
+[{'score': 0.425, 'answer': 'us-001', 'start': 16, 'end': 16}]
 ```
 
 <Tip>

docs/source/en/task_summary.md

@@ -326,7 +326,7 @@ Document question answering is a task that answers natural language questions fr
 >>> from PIL import Image
 >>> import requests
 
->>> url = "https://datasets-server.huggingface.co/assets/hf-internal-testing/example-documents/--/hf-internal-testing--example-documents/test/2/image/image.jpg"
+>>> url = "https://huggingface.co/datasets/hf-internal-testing/example-documents/resolve/main/jpeg_images/2.jpg"
 >>> image = Image.open(requests.get(url, stream=True).raw)
 
 >>> doc_question_answerer = pipeline("document-question-answering", model="magorshunov/layoutlm-invoices")