questiongenerator.py

import os
import sys
import math
import numpy as np
import torch
import spacy
import re
import random
import json
import en_core_web_sm
from string import punctuation

#from transformers import T5Tokenizer, T5ForConditionalGeneration, T5Config
#from transformers import BertTokenizer, BertForSequenceClassification
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
class QuestionGenerator():

    def __init__(self, model_dir=None):

        QG_PRETRAINED = 'iarfmoose/t5-base-question-generator'
        self.ANSWER_TOKEN = '<answer>'
        self.CONTEXT_TOKEN = '<context>'
        self.SEQ_LENGTH = 512

        self.device = torch.device('cpu')
        # self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

        self.qg_tokenizer = AutoTokenizer.from_pretrained(QG_PRETRAINED)
        self.qg_model = AutoModelForSeq2SeqLM.from_pretrained(QG_PRETRAINED)
        self.qg_model.to(self.device)

        self.qa_evaluator = QAEvaluator(model_dir)

    def generate(self, article, use_evaluator=True, num_questions=None, answer_style='all'):

        print("Generating questions...\n")

        qg_inputs, qg_answers = self.generate_qg_inputs(article, answer_style)
        print("qg_inputs, qg_answers=>",qg_inputs, qg_answers)
        generated_questions = self.generate_questions_from_inputs(qg_inputs,num_questions)
        print("generated_questions(generate)=>",generated_questions)
        return generated_questions
        message = "{} questions doesn't match {} answers".format(
            len(generated_questions),
            len(qg_answers))
        assert len(generated_questions) == len(qg_answers), message

        if use_evaluator:

            print("Evaluating QA pairs...\n")

            encoded_qa_pairs = self.qa_evaluator.encode_qa_pairs(generated_questions, qg_answers)
            scores = self.qa_evaluator.get_scores(encoded_qa_pairs)
            if num_questions:
                qa_list = self._get_ranked_qa_pairs(generated_questions, qg_answers, scores, num_questions)
            else:
                qa_list = self._get_ranked_qa_pairs(generated_questions, qg_answers, scores)

        else:
            print("Skipping evaluation step.\n")
            qa_list = self._get_all_qa_pairs(generated_questions, qg_answers)

        return qa_list

    def generate_qg_inputs(self, text, answer_style):

        VALID_ANSWER_STYLES = ['all', 'sentences', 'multiple_choice']

        if answer_style not in VALID_ANSWER_STYLES:
            raise ValueError(
                "Invalid answer style {}. Please choose from {}".format(
                    answer_style,
                    VALID_ANSWER_STYLES
                )
            )

        inputs = []
        answers = []

        if answer_style == 'sentences' or answer_style == 'all':
            segments = self._split_into_segments(text)
            for segment in segments:
                sentences = self._split_text(segment)
                prepped_inputs, prepped_answers = self._prepare_qg_inputs(sentences, segment)
                inputs.extend(prepped_inputs)
                answers.extend(prepped_answers)

        if answer_style == 'multiple_choice' or answer_style == 'all':
            sentences = self._split_text(text)
            prepped_inputs, prepped_answers = self._prepare_qg_inputs_MC(sentences)
            inputs.extend(prepped_inputs)
            answers.extend(prepped_answers)

        return inputs, answers

    def generate_questions_from_inputs(self, qg_inputs,num_questions):
        generated_questions = []
        count = 0
        print("num que => ", num_questions)
        for qg_input in qg_inputs:
            if count < int(num_questions):
                question = self._generate_question(qg_input)

                question = question.strip()                 #remove trailing spaces
                question = question.strip(punctuation)      #remove trailing questionmarks
                question += "?"                             #add one ?
                if question not in generated_questions:
                    generated_questions.append(question)
                    print("question ===> ",question)
                    count += 1
            else:
                return generated_questions
        return generated_questions #
    def _split_text(self, text):
        MAX_SENTENCE_LEN = 128

        sentences = re.findall('.*?[.!\?]', text)

        cut_sentences = []
        for sentence in sentences:
            if len(sentence) > MAX_SENTENCE_LEN:
                cut_sentences.extend(re.split('[,;:)]', sentence))
        # temporary solution to remove useless post-quote sentence fragments
        cut_sentences = [s for s in sentences if len(s.split(" ")) > 5]
        sentences = sentences + cut_sentences

        return list(set([s.strip(" ") for s in sentences]))

    def _split_into_segments(self, text):
        MAX_TOKENS = 490

        paragraphs = text.split('\n')
        tokenized_paragraphs = [self.qg_tokenizer(p)['input_ids'] for p in paragraphs if len(p) > 0]

        segments = []
        while len(tokenized_paragraphs) > 0:
            segment = []
            while len(segment) < MAX_TOKENS and len(tokenized_paragraphs) > 0:
                paragraph = tokenized_paragraphs.pop(0)
                segment.extend(paragraph)
            segments.append(segment)
        return [self.qg_tokenizer.decode(s) for s in segments]

    def _prepare_qg_inputs(self, sentences, text):
        inputs = []
        answers = []

        for sentence in sentences:
            qg_input = '{} {} {} {}'.format(
                self.ANSWER_TOKEN,
                sentence,
                self.CONTEXT_TOKEN,
                text
            )
            inputs.append(qg_input)
            answers.append(sentence)

        return inputs, answers

    def _prepare_qg_inputs_MC(self, sentences):

        spacy_nlp = en_core_web_sm.load()
        docs = list(spacy_nlp.pipe(sentences, disable=['parser']))
        inputs_from_text = []
        answers_from_text = []

        for i in range(len(sentences)):
            entities = docs[i].ents
            if entities:
                for entity in entities:
                    qg_input = '{} {} {} {}'.format(
                        self.ANSWER_TOKEN,
                        entity,
                        self.CONTEXT_TOKEN,
                        sentences[i]
                    )
                    answers = self._get_MC_answers(entity, docs)
                    inputs_from_text.append(qg_input)
                    answers_from_text.append(answers)

        return inputs_from_text, answers_from_text

    def _get_MC_answers(self, correct_answer, docs):

        entities = []
        for doc in docs:
            entities.extend([{'text': e.text, 'label_': e.label_} for e in doc.ents])

        # remove duplicate elements
        entities_json = [json.dumps(kv) for kv in entities]
        pool = set(entities_json)
        num_choices = min(4, len(pool)) - 1  # -1 because we already have the correct answer

        # add the correct answer
        final_choices = []
        correct_label = correct_answer.label_
        final_choices.append({'answer': correct_answer.text, 'correct': True})
        pool.remove(json.dumps({'text': correct_answer.text, 'label_': correct_answer.label_}))

        # find answers with the same NER label
        matches = [e for e in pool if correct_label in e]

        # if we don't have enough then add some other random answers
        if len(matches) < num_choices:
            choices = matches
            pool = pool.difference(set(choices))
            choices.extend(random.sample(pool, num_choices - len(choices)))
        else:
            choices = random.sample(matches, num_choices)

        choices = [json.loads(s) for s in choices]
        for choice in choices:
            final_choices.append({'answer': choice['text'], 'correct': False})
        random.shuffle(final_choices)
        return final_choices

    def _generate_question(self, qg_input):
        self.qg_model.eval()
        encoded_input = self._encode_qg_input(qg_input)
        with torch.no_grad():
            output = self.qg_model.generate(input_ids=encoded_input['input_ids'])
        return self.qg_tokenizer.decode(output[0])

    def _encode_qg_input(self, qg_input):
        return self.qg_tokenizer(
            qg_input,
            pad_to_max_length=True,
            max_length=self.SEQ_LENGTH,
            truncation=True,
            return_tensors="pt"
        ).to(self.device)

    def _get_ranked_qa_pairs(self, generated_questions, qg_answers, scores, num_questions=10):
        if num_questions > len(scores):
            num_questions = len(scores)
            print("\nWas only able to generate {} questions. For more questions, please input a longer text.".format(num_questions))

        qa_list = []
        for i in range(num_questions):
            index = scores[i]
            qa = self._make_dict(
                generated_questions[index].split('?')[0] + '?',
                qg_answers[index])
            qa_list.append(qa)
        return qa_list

    def _get_all_qa_pairs(self, generated_questions, qg_answers):
        qa_list = []
        for i in range(len(generated_questions)):
            qa = self._make_dict(
                generated_questions[i].split('?')[0] + '?',
                qg_answers[i])
            qa_list.append(qa)
        return qa_list

    def _make_dict(self, question, answer):
        qa = {}
        qa['question'] = question
        qa['answer'] = answer
        return qa


class QAEvaluator():
    def __init__(self, model_dir=None):

        QAE_PRETRAINED = 'iarfmoose/bert-base-cased-qa-evaluator'
        self.SEQ_LENGTH = 512

        self.device = torch.device('cpu')
        # self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

        self.qae_tokenizer = AutoTokenizer.from_pretrained(QAE_PRETRAINED)
        self.qae_model = AutoModelForSequenceClassification.from_pretrained(QAE_PRETRAINED)
        self.qae_model.to(self.device)


    def encode_qa_pairs(self, questions, answers):
        encoded_pairs = []
        for i in range(len(questions)):
            encoded_qa = self._encode_qa(questions[i], answers[i])
            encoded_pairs.append(encoded_qa.to(self.device))
        return encoded_pairs

    def get_scores(self, encoded_qa_pairs):
        scores = {}
        self.qae_model.eval()
        with torch.no_grad():
            for i in range(len(encoded_qa_pairs)):
                scores[i] = self._evaluate_qa(encoded_qa_pairs[i])

        return [k for k, v in sorted(scores.items(), key=lambda item: item[1], reverse=True)]

    def _encode_qa(self, question, answer):
        if type(answer) is list:
            for a in answer:
                if a['correct']:
                    correct_answer = a['answer']
        else:
            correct_answer = answer
        return self.qae_tokenizer(
            text=question,
            text_pair=correct_answer,
            pad_to_max_length=True,
            max_length=self.SEQ_LENGTH,
            truncation=True,
            return_tensors="pt"
        )

    def _evaluate_qa(self, encoded_qa_pair):
        output = self.qae_model(**encoded_qa_pair)
        return output[0][0][1]


def print_qa(qa_list, show_answers=True):
    for i in range(len(qa_list)):
        space = ' ' * int(np.where(i < 9, 3, 4)) # wider space for 2 digit q nums

        print('{}) Q: {}'.format(i + 1, qa_list[i]['question']))

        answer = qa_list[i]['answer']

        # print a list of multiple choice answers
        if type(answer) is list:

            if show_answers:
                print('{}A: 1.'.format(space),
                      answer[0]['answer'],
                      np.where(answer[0]['correct'], '(correct)', ''))
                for j in range(1, len(answer)):
                    print('{}{}.'.format(space + '   ', j + 1),
                          answer[j]['answer'],
                          np.where(answer[j]['correct'] == True, '(correct)', ''))

            else:
                print('{}A: 1.'.format(space),
                      answer[0]['answer'])
                for j in range(1, len(answer)):
                    print('{}{}.'.format(space + '   ', j + 1),
                          answer[j]['answer'])
            print('')

        # print full sentence answers
        else:
            if show_answers:
                print('{}A:'.format(space), answer, '\n')