# python standard library
import random
from  datetime import datetime
import logging

# libraries
import networkx as nx

# this project
# import questions

# setup logger for this module
logger = logging.getLogger(__name__)

# ----------------------------------------------------------------------------
# kowledge state of each student....??
# Contains:
#   state - dict of topics with state of unlocked topics
#   deps  - dependency graph
#   topic_sequence - list with the order of recommended topics
# ----------------------------------------------------------------------------
class StudentKnowledge(object):
    # =======================================================================
    # methods that update state
    # =======================================================================
    def __init__(self, deps, state={}):
        self.deps = deps  # dependency graph shared among students
        self.state = state # {'topic': {'level':0.5, 'date': datetime}, ...}

        self.update_topic_levels() # forgetting factor
        self.topic_sequence = self.recommend_topic_sequence() # ['a', 'b', ...]
        self.unlock_topics() # whose dependencies have been done
        self.current_topic = None

        self.MAX_QUESTIONS = 6  # FIXME get from yaml configuration file??

    # ------------------------------------------------------------------------
    # Updates the proficiency levels of the topics, with forgetting factor
    # FIXME no dependencies are considered yet...
    # ------------------------------------------------------------------------
    def update_topic_levels(self):
        now = datetime.now()
        for s in self.state.values():
            dt = now - s['date']
            s['level'] *= 0.95 ** dt.days   # forgetting factor 0.95


    # ------------------------------------------------------------------------
    # Unlock topics whose dependencies are satisfied (> min_level)
    # ------------------------------------------------------------------------
    def unlock_topics(self):
        # minimum level that the dependencies of a topic must have
        # for the topic to be unlocked.
        min_level = 0.01

        for topic in self.topic_sequence:
            if topic not in self.state:  # if locked
                pred = self.deps.predecessors(topic)
                if all(d in self.state and self.state[d]['level'] > min_level for d in pred):
                    # all dependencies are done
                    self.state[topic] = {
                        'level': 0.0,           # unlocked
                        'date': datetime.now()
                        }
                    logger.debug(f'Unlocked "{topic}".')


    # ------------------------------------------------------------------------
    # Start a new topic. If not provided, gets a recommendation.
    #    questions: list of generated questions to do in the topic
    #    current_question: the current question to be presented
    # ------------------------------------------------------------------------
    def init_topic(self, topic=''):
        logger.debug(f'StudentKnowledge.init_topic({topic})')

        # maybe get topic recommendation
        if not topic:
            topic = self.get_recommended_topic()
            logger.debug(f'Recommended topic is {topic}')

        # do not allow locked topics
        if self.is_locked(topic):
            logger.debug(f'Topic {topic} is locked')
            return

        # starting new topic
        self.current_topic = topic

        factory = self.deps.node[topic]['factory']
        questionlist = self.deps.node[topic]['questions']

        self.correct_answers = 0
        self.wrong_answers = 0

        # select a random set of questions for this topic
        size = min(self.MAX_QUESTIONS, len(questionlist)) # number of questions
        questionlist = random.sample(questionlist, k=size)
        logger.debug(f'Questions: {", ".join(questionlist)}')

        # generate instances of questions
        self.questions = [factory[qref].generate() for qref in questionlist]
        logger.debug(f'Total: {len(self.questions)} questions')

        # get first question
        self.next_question()


    # def init_learning(self, topic=''):
    #     logger.debug(f'StudentKnowledge.init_learning({topic})')

    #     if self.is_locked(topic):
    #         logger.debug(f'Topic {topic} is locked')
    #         return False

    #     self.current_topic = topic
    #     factory = self.deps.node[topic]['factory']
    #     lesson = self.deps.node[topic]['lesson']

    #     self.questions = [factory[qref].generate() for qref in lesson]
    #     logger.debug(f'Total: {len(self.questions)} questions')

    #     self.next_question_in_lesson()

    # ------------------------------------------------------------------------
    # The topic has finished and there are no more questions.
    # The topic level is updated in state and unlocks are performed.
    # The current topic is unchanged.
    # ------------------------------------------------------------------------
    def finish_topic(self):
        logger.debug(f'StudentKnowledge.finish_topic({self.current_topic})')

        self.current_question = None
        self.state[self.current_topic] = {
            'date': datetime.now(),
            'level': self.correct_answers / (self.correct_answers + self.wrong_answers)
            }
        self.unlock_topics()


    # ------------------------------------------------------------------------
    # corrects current question with provided answer.
    # implements the logic:
    #   - if answer ok, goes to next question
    #   - if wrong, counts number of tries. If exceeded, moves on.
    # ------------------------------------------------------------------------
    def check_answer(self, answer):
        logger.debug('StudentKnowledge.check_answer()')

        q = self.current_question
        q['answer'] = answer
        q['finish_time'] = datetime.now()
        grade = q.correct()

        logger.debug(f'Grade {grade:.2} ({q["ref"]})')

        # if answer is correct, get next question
        if grade > 0.999:
            self.correct_answers += 1
            self.next_question()

        # if wrong, keep same question and append a similar one at the end
        else:
            self.wrong_answers += 1

            self.current_question['tries'] -= 1

            logger.debug(f'Wrong answers = {self.wrong_answers};  Tries = {self.current_question["tries"]}')

            # append a new instance of the current question to the end and
            # move to the next question
            if self.current_question['tries'] <= 0:
                logger.debug("Appending new instance of this question to the end")
                factory = self.deps.node[self.current_topic]['factory']
                self.questions.append(factory[q['ref']].generate())
                self.next_question()

        # returns answered and corrected question (not new one)
        return q

    # ------------------------------------------------------------------------
    # Move to next question
    # ------------------------------------------------------------------------
    def next_question(self):
        try:
            self.current_question = self.questions.pop(0)
        except IndexError:
            self.finish_topic()
        else:
            self.current_question['start_time'] = datetime.now()
            self.current_question['tries'] = self.current_question.get('max_tries', 3) # FIXME hardcoded 3
            logger.debug(f'Next question is "{self.current_question["ref"]}"')

    # def next_question_in_lesson(self):
    #     try:
    #         self.current_question = self.questions.pop(0)
    #     except IndexError:
    #         self.current_question = None
    #     else:
    #         logger.debug(f'Next question is "{self.current_question["ref"]}"')


    # ========================================================================
    # pure functions of the state (no side effects)
    # ========================================================================


    # ------------------------------------------------------------------------
    # compute recommended sequence of topics  ['a', 'b', ...]
    # ------------------------------------------------------------------------
    def recommend_topic_sequence(self):
        return list(nx.topological_sort(self.deps))

    # ------------------------------------------------------------------------
    def get_current_question(self):
        return self.current_question

    # ------------------------------------------------------------------------
    def get_current_topic(self):
        return self.current_topic

    # ------------------------------------------------------------------------
    def is_locked(self, topic):
        return topic not in self.state

    # ------------------------------------------------------------------------
    # Return list of {ref: 'xpto', name: 'long name', leve: 0.5}
    # Levels are in the interval [0, 1] if unlocked or None if locked.
    # Topics unlocked but not yet done have level 0.0.
    # ------------------------------------------------------------------------
    def get_knowledge_state(self):
        return [{
            'ref': ref,
            'type': self.deps.nodes[ref]['type'],
            'name': self.deps.nodes[ref]['name'],
            'level': self.state[ref]['level'] if ref in self.state else None
            } for ref in self.topic_sequence ]

    # ------------------------------------------------------------------------
    def get_topic_progress(self):
        return self.correct_answers / (1 + self.correct_answers + len(self.questions))

    # ------------------------------------------------------------------------
    def get_topic_level(self, topic):
        return self.state[topic]['level']

    # ------------------------------------------------------------------------
    def get_topic_date(self, topic):
        return self.state[topic]['date']

    # ------------------------------------------------------------------------
    # Recommends a topic to practice/learn from the state.
    # ------------------------------------------------------------------------
    def get_recommended_topic(self):    # FIXME untested
        return min(self.state.items(), key=lambda x: x[1]['level'])[0]