def read_squad_examples(input_file, is_training):
    """Read a SQuAD json file into a list of SquadExample."""
    with open(input_file, "r", encoding='utf-8') as reader:
        input_data = json.load(reader)["data"]

    def is_whitespace(c):
        if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
            return True
        return False

    examples = []
    for entry in input_data:
        for paragraph in entry["paragraphs"]:
            paragraph_text = paragraph["context"]
            doc_tokens = []
            char_to_word_offset = []
            prev_is_whitespace = True
            for c in paragraph_text:
                if is_whitespace(c):
                    prev_is_whitespace = True
                else:
                    if prev_is_whitespace:
                        doc_tokens.append(c)
                    else:
                        doc_tokens[-1] += c
                    prev_is_whitespace = False
                char_to_word_offset.append(len(doc_tokens) - 1)

            for qa in paragraph["qas"]:
                qas_id = qa["id"]
                question_text = qa["question"]
                start_position = None
                end_position = None
                orig_answer_text = None
                if is_training:
                    if len(qa["answers"]) != 1:
                        raise ValueError(
                            "For training, each question should have exactly 1 answer."
                        )
                    answer = qa["answers"][0]
                    orig_answer_text = answer["text"]
                    answer_offset = answer["answer_start"]
                    answer_length = len(orig_answer_text)
                    start_position = char_to_word_offset[answer_offset]
                    end_position = char_to_word_offset[answer_offset +
                                                       answer_length - 1]
                    # Only add answers where the text can be exactly recovered from the
                    # document. If this CAN'T happen it's likely due to weird Unicode
                    # stuff so we will just skip the example.
                    #
                    # Note that this means for training mode, every example is NOT
                    # guaranteed to be preserved.
                    actual_text = " ".join(
                        doc_tokens[start_position:(end_position + 1)])
                    cleaned_answer_text = " ".join(
                        whitespace_tokenize(orig_answer_text))
                    if actual_text.find(cleaned_answer_text) == -1:
                        logger.warning("Could not find answer: '%s' vs. '%s'",
                                       actual_text, cleaned_answer_text)
                        continue

                example = SquadExample(qas_id=qas_id,
                                       question_text=question_text,
                                       doc_tokens=doc_tokens,
                                       orig_answer_text=orig_answer_text,
                                       start_position=start_position,
                                       end_position=end_position)
                examples.append(example)
    return examples
Read a SQuAD json file into a list of SquadExample.
training/BingBertSquad/nvidia_run_squad_deepspeed.py