Json – How should I preprocess this dataset for performing a "Question-Answering" task? Pytorch

So I have a JSON dataset that is in the following format:

[{
    "answer": "...",
    "question": "...",
    "context": "..."
  },
 ...
]

All of the fields are normal plaintext. My goal is train a pretrained BERT model on it for the task of "Question-Answering". When reviewing through the HuggingFace docs it seems that the formatting needs to match the Squad dataset formatting, context needs to be trimmed to a max of 394 characters, and things like [CLS] and other tokens need to be added.

When trying to follow the docs, I get to this site for question answering and they provide a sample function to do the preprocessing. Provided here for simplicity:

def preprocess_function(examples):
    questions = [q.strip() for q in examples["question"]]
    inputs = tokenizer(
        questions,
        examples["context"],
        max_length=384,
        truncation="only_second",
        return_offsets_mapping=True,
        padding="max_length",
    )

    offset_mapping = inputs.pop("offset_mapping")
    answers = examples["answers"]
    start_positions = []
    end_positions = []

    for i, offset in enumerate(offset_mapping):
        answer = answers[i]
        start_char = answer["answer_start"][0]
        end_char = answer["answer_start"][0] + len(answer["text"][0])
        sequence_ids = inputs.sequence_ids(i)

        # Find the start and end of the context
        idx = 0
        while sequence_ids[idx] != 1:
            idx += 1
        context_start = idx
        while sequence_ids[idx] == 1:
            idx += 1
        context_end = idx - 1

        # If the answer is not fully inside the context, label it (0, 0)
        if offset[context_start][0] > end_char or offset[context_end][1] < start_char:
            start_positions.append(0)
            end_positions.append(0)
        else:
            # Otherwise it's the start and end token positions
            idx = context_start
            while idx <= context_end and offset[idx][0] <= start_char:
                idx += 1
            start_positions.append(idx - 1)

            idx = context_end
            while idx >= context_start and offset[idx][1] >= end_char:
                idx -= 1
            end_positions.append(idx + 1)

    inputs["start_positions"] = start_positions
    inputs["end_positions"] = end_positions
    return inputs

With the following sample code:

from transformers import AutoTokenizer
from datasets import Dataset

dataset = Dataset.from_pandas(df) # where df is a 3 column Dataframe Object | answer | question | context |

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
tokenized_ds = dataset.map(preprocess_function, batched=True)

I get the following error when I try to run it.

File "c:/project.py", line 234, in preprocess_function
    inputs = tokenizer(
TypeError: 'list' object is not callable

So my question: Is this just a silly error on my part, or should I be trying to preprocess this dataset in a different manner? I am new to HuggingFace and NLP in general so I am doing this as a fun project. Thanks in advance!

UPDATE 1: Following the tutorial more closely I put my data into a Datasets object and used the map function properly, but now it is saying the tokenizer is a problem because TypeError: 'list' object is not callable but it is the exact same tokenizer used in the tutorial.