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# CodeQ: an online programming tutor.
# Copyright (C) 2016,2017 UL FRI
#
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU Affero General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option) any
# later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more
# details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.

import collections
from itertools import chain, combinations, product
import pickle
import random
import sys

from nltk import ParentedTree, Tree

from prolog.util import parse as prolog_parse

# construct pattern to match the structure of nodes given by [include],
# supports variables and literals
def pattern(node, include):
    if not isinstance(node, Tree):
        return None

    label = node.label()
    if any(n is node for n in include):
        if label == 'literal':
            return '"{}"'.format(node[0].val)
        if label == 'variable':
            return '{}'.format(label)
        return None
    if label == 'functor':
        return '({} "{}")'.format(label, node[0].val)

    subpats = [pattern(child, include) for child in node]
    pat = None
    if any(subpats):
        if label == 'and':
            if subpats[1]:
                pat = subpats[1]
            if subpats[0]:
                if pat:
                    pat = subpats[0] + ' ' + pat
                else:
                    pat = subpats[0]
        elif label == 'args':
            pat = label
            for i, subpat in enumerate(subpats):
                if subpat:
                    pat += ' {}'.format(subpat)
            pat = '(' + pat + ')'
        elif label == 'unop':
            pat = '(' + label + ' ' + node[0].val + ' ' + subpats[1] + ')'
        elif label == 'binop':
            pat = label
            if subpats[0]:
                pat += ' {}'.format(subpats[0])
            pat += ' "{}"'.format(node[1].val)
            if subpats[2]:
                pat += ' {}'.format(subpats[2])
            pat = '(' + pat + ')'
        elif label == 'clause':
            pat = label
            for i, subpat in enumerate(subpats):
                if subpat:
                    pat += ' {}'.format(subpats[i])
            return '(' + pat + ')'
        elif label == 'compound':
            if len(subpats) > 1 and subpats[1]:
                pat = label
                for i, subpat in enumerate(subpats):
                    pat += ' {}'.format(subpat)
                pat = '(' + pat + ')'
            else:
                return None
        elif label == 'head':
            pat = label
            pat += ' {}'.format(subpats[0])
            pat = '(' + pat + ')'
        elif label == 'list':
            pat = 'list '
            if subpats[0]:
                pat += '(h {})'.format(subpats[0])
            if subpats[0] and subpats[1]:
                pat += ' '
            if subpats[1]:
                pat += '(t {})'.format(subpats[1])
            pat = '(' + pat + ')'
        if not pat:
            for s in subpats:
                if s:
                    pat = s
                    break
    return pat

def get_patterns(tree):
    if isinstance(tree, str):
        tree = prolog_parse(tree)
        if tree is None:
            return
    tree = ParentedTree.convert(tree)

    # get patterns separately for each clause
    for clause in tree:
        # collect variable nodes in this clause
        variables = collections.defaultdict(list)
        for node in clause.subtrees():
            if isinstance(node, Tree) and node.label() == 'variable':
                name = node[0].val
                variables[name].append(node)

        # yield patterns for singleton variables
        for var, nodes in variables.items():
            if len(nodes) == 1:
                pat = pattern(clause, nodes)
                if pat:
                    yield pat, nodes

        # yield patterns for variable-variable pairs (within a clause)
        for var, nodes in variables.items():
            for selected in combinations(nodes, 2):
                pat = pattern(clause, selected)
                if pat:
                    yield pat, selected
        
        # yield patterns for variable-literal / literal-literal pairs
        # yield patterns for singleton literals
        # (only within a topmost compound / binop / unop)
        def patterns_with_literals(node):
            if not isinstance(node, Tree):
                return
            if node.label() in {'compound', 'binop', 'unop'}:
                vars = [n for n in node.subtrees() if n.label() == 'variable']
                lits = [n for n in node.subtrees() if n.label() == 'literal']
                for selected in chain(combinations(lits, 1), 
                                      combinations(lits, 2), 
                                      product(lits, vars)):
                    pat = pattern(clause, selected)
                    if pat:
                        yield pat, selected
            else:
                for child in node:
                    yield from patterns_with_literals(child)
        yield from patterns_with_literals(clause)

# Extract edits and other data from existing traces for each problem.
# Run with: python3 -m monkey.patterns <problem ID> <predicate name> <solutions.pickle>
if __name__ == '__main__':
    pid = int(sys.argv[1])
    name = sys.argv[2]
    submissions = pickle.load(open('pickle/programs-{}.pickle'.format(pid), 'rb'))

    # find test/train users
    users = sorted({user for code, info in submissions.items() for user in info['users']})
    random.Random(0).shuffle(users)
    split = int(len(users)*0.7)
    learn_users = set(users[:split])
    test_users = set(users[split:])

    # save test users to file
    with open('data/users-test-{}.txt'.format(pid), 'wt') as f:
        for user in test_users:
            print(user, file=f)

    # find test/train programs
    data = {
        'train': [],
        'test': []
    }
    for code, info in submissions.items():
        if len(code) > 1000 or prolog_parse(code) is None:
            continue
        if name not in code:
            continue
        data['train'] += [(code, info['n_tests'] == info['n_passed'])] * len(info['users'] & learn_users)
        data['test'] += [(code, info['n_tests'] == info['n_passed'])] * len(info['users'] & test_users)

    # print info about test users and test/train programs
    print('Test users:')
    print(test_users)
    print()
    for which in ['train', 'test']:
        print('Programs ({}):'.format(which))
        print('correct: {} ({} unique)'.format(
            len([code for code, correct in data[which] if correct]),
            len({code for code, correct in data[which] if correct})))
        print('incorrect: {} ({} unique)'.format(
            len([code for code, correct in data[which] if not correct]),
            len({code for code, correct in data[which] if not correct})))
        print()

    # extract attributes from training data
    patterns = collections.Counter()
    for code, correct in data['train']:
        for pat, nodes in get_patterns(code):
            patterns[pat] += 1

    attrs = []
    with open('data/attributes-{:03}.tab'.format(pid), 'w') as pattern_file:
        for i, (pat, count) in enumerate(patterns.most_common()):
            if count < 5:
                break
            attrs.append(pat)
            print('a{}\t{}'.format(i, pat), file=pattern_file)

    # check and write attributes for training/test data
    for t in ['train', 'test']:
        with open('data/programs-{:03}-{}.tab'.format(pid, t), 'w') as f:
            # print header
            print('\t'.join(['code', 'correct'] + ['a'+str(i) for i in range(len(attrs))]), file=f)
            print('\t'.join(['d'] * (len(attrs)+2)), file=f)
            print('meta\tclass', file=f)

            # print rows (program, correct, attr1, attr2, …)
            for code, correct in data[t]:
                record = '{}\t{}'.format(repr(code), 'T' if correct else 'F')
                code_pats = [pat for pat, nodes in get_patterns(code)]
                for pat in attrs:
                    record += '\t{}'.format('T' if pat in code_pats else 'F')
                print(record, file=f)