"""
This code is slightly modified from Hengyuan Hu's repository.
https://github.com/hengyuan-hu/bottom-up-attention-vqa
"""
from __future__ import print_function
import os
import sys
import json
import numpy as np
import re
import _pickle as cPickle

sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
# from dataset import Dictionary
import utils


contractions = {
    "aint": "ain't", "arent": "aren't", "cant": "can't", "couldve":
    "could've", "couldnt": "couldn't", "couldn'tve": "couldn't've",
    "couldnt've": "couldn't've", "didnt": "didn't", "doesnt":
    "doesn't", "dont": "don't", "hadnt": "hadn't", "hadnt've":
    "hadn't've", "hadn'tve": "hadn't've", "hasnt": "hasn't", "havent":
    "haven't", "hed": "he'd", "hed've": "he'd've", "he'dve":
    "he'd've", "hes": "he's", "howd": "how'd", "howll": "how'll",
    "hows": "how's", "Id've": "I'd've", "I'dve": "I'd've", "Im":
    "I'm", "Ive": "I've", "isnt": "isn't", "itd": "it'd", "itd've":
    "it'd've", "it'dve": "it'd've", "itll": "it'll", "let's": "let's",
    "maam": "ma'am", "mightnt": "mightn't", "mightnt've":
    "mightn't've", "mightn'tve": "mightn't've", "mightve": "might've",
    "mustnt": "mustn't", "mustve": "must've", "neednt": "needn't",
    "notve": "not've", "oclock": "o'clock", "oughtnt": "oughtn't",
    "ow's'at": "'ow's'at", "'ows'at": "'ow's'at", "'ow'sat":
    "'ow's'at", "shant": "shan't", "shed've": "she'd've", "she'dve":
    "she'd've", "she's": "she's", "shouldve": "should've", "shouldnt":
    "shouldn't", "shouldnt've": "shouldn't've", "shouldn'tve":
    "shouldn't've", "somebody'd": "somebodyd", "somebodyd've":
    "somebody'd've", "somebody'dve": "somebody'd've", "somebodyll":
    "somebody'll", "somebodys": "somebody's", "someoned": "someone'd",
    "someoned've": "someone'd've", "someone'dve": "someone'd've",
    "someonell": "someone'll", "someones": "someone's", "somethingd":
    "something'd", "somethingd've": "something'd've", "something'dve":
    "something'd've", "somethingll": "something'll", "thats":
    "that's", "thered": "there'd", "thered've": "there'd've",
    "there'dve": "there'd've", "therere": "there're", "theres":
    "there's", "theyd": "they'd", "theyd've": "they'd've", "they'dve":
    "they'd've", "theyll": "they'll", "theyre": "they're", "theyve":
    "they've", "twas": "'twas", "wasnt": "wasn't", "wed've":
    "we'd've", "we'dve": "we'd've", "weve": "we've", "werent":
    "weren't", "whatll": "what'll", "whatre": "what're", "whats":
    "what's", "whatve": "what've", "whens": "when's", "whered":
    "where'd", "wheres": "where's", "whereve": "where've", "whod":
    "who'd", "whod've": "who'd've", "who'dve": "who'd've", "wholl":
    "who'll", "whos": "who's", "whove": "who've", "whyll": "why'll",
    "whyre": "why're", "whys": "why's", "wont": "won't", "wouldve":
    "would've", "wouldnt": "wouldn't", "wouldnt've": "wouldn't've",
    "wouldn'tve": "wouldn't've", "yall": "y'all", "yall'll":
    "y'all'll", "y'allll": "y'all'll", "yall'd've": "y'all'd've",
    "y'alld've": "y'all'd've", "y'all'dve": "y'all'd've", "youd":
    "you'd", "youd've": "you'd've", "you'dve": "you'd've", "youll":
    "you'll", "youre": "you're", "youve": "you've"
}

# list of 172 stop-words. (179 stopwords from nltk - '{'no', 'both', 'above', 'below', 'after', 'before', 'between'})
# stopwords = ['i', 'me', 'my', 'myself', 'we', 'our', 'ours',
#              'ourselves', 'you', "you're", "you've", "you'll",
#              "you'd", 'your', 'yours', 'yourself', 'yourselves',
#              'he', 'him', 'his', 'himself', 'she', "she's", 'her',
#              'hers', 'herself', 'it', "it's", 'its', 'itself', 'they',
#              'them', 'their', 'theirs', 'themselves', 'what', 'which',
#              'who', 'whom', 'this', 'that', "that'll", 'these', 'those',
#              'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being',
#              'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing',
#              'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as',
#              'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about',
#              'against', 'into', 'through', 'during',
#              'to', 'from', 'up', 'down', 'in',
#              'out', 'on', 'off', 'over', 'under', 'again', 'further',
#              'then', 'once', 'here', 'there', 'when', 'where', 'why',
#              'how', 'all', 'any', 'each', 'few', 'more',
#              'most', 'other', 'some', 'such', 'nor', 'not',
#              'only', 'own', 'same', 'so', 'than', 'too', 'very',
#              's', 't', 'can', 'will', 'just', 'don', "don't",
#              'should', "should've", 'now', 'd', 'll', 'm', 'o',
#              're', 've', 'y', 'ain', 'aren', "aren't", 'couldn',
#              "couldn't", 'didn', "didn't", 'doesn', "doesn't",
#              'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't",
#              'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn',
#              "mustn't", 'needn', "needn't", 'shan', "shan't",
#              'shouldn', "shouldn't", 'wasn', "wasn't", 'weren',
#              "weren't", 'won', "won't", 'wouldn', "wouldn't"]

manual_map = { 'none': '0',
              'zero': '0',
              'one': '1',
              'two': '2',
              'three': '3',
              'four': '4',
              'five': '5',
              'six': '6',
              'seven': '7',
              'eight': '8',
               'nine': '9',
              'ten': '10'}
articles = ['a', 'an', 'the']
period_strip = re.compile("(?!<=\d)(\.)(?!\d)")
comma_strip = re.compile("(\d)(\,)(\d)")
punct = [';', r"/", '[', ']', '"', '{', '}',
                '(', ')', '=', '+', '\\', '_', '-',
                '>', '<', '@', '`', ',', '?', '!']


# Notice that VQA score is the average of 10 choose 9 candidate answers cases
# See http://visualqa.org/evaluation.html
def get_score(occurences):
    if occurences == 0:
        return .0
    elif occurences == 1:
        return .3
    elif occurences == 2:
        return .6
    elif occurences == 3:
        return .9
    else:
        return 1.


def process_punctuation(inText):
    outText = inText
    for p in punct:
        if (p + ' ' in inText or ' ' + p in inText) \
           or (re.search(comma_strip, inText) != None):
            outText = outText.replace(p, '')
        else:
            outText = outText.replace(p, ' ')
    outText = period_strip.sub("", outText, re.UNICODE)
    return outText


def process_digit_article(inText):
    outText = []
    tempText = inText.lower().split()
    for word in tempText:
        word = manual_map.setdefault(word, word)
        if word not in articles:
            outText.append(word)
        else:
            pass
    for wordId, word in enumerate(outText):
        if word in contractions:
            outText[wordId] = contractions[word]
    outText = ' '.join(outText)
    return outText


def multiple_replace(text, wordDict):
    for key in wordDict:
        text = text.replace(key, wordDict[key])
    return text


def preprocess_answer(answer):
    answer = str(answer)
    answer = process_digit_article(process_punctuation(answer))
    answer = answer.replace(',', '').replace('x ray', 'xray')
    return answer


def filter_answers(answers_dset, min_occurence):
    """This will change the answer to preprocessed version
    """
    occurence = {}

    for ans_entry in answers_dset:
        answers = ans_entry['answer']
        gtruth = ans_entry['answer']
        gtruth = preprocess_answer(gtruth)
        if gtruth not in occurence:
            occurence[gtruth] = set()
        occurence[gtruth].add(ans_entry['qid'])
    for answer in list(occurence):
        if len(occurence[answer]) < min_occurence:
            occurence.pop(answer)

    print('Num of answers that appear >= %d times: %d' % (
        min_occurence, len(occurence)))
    return occurence


def create_ans2label(occurence, name, cache_root='data_RAD/cache'):
    """Note that this will also create label2ans.pkl at the same time

    occurence: dict {answer -> whatever}
    name: prefix of the output file
    cache_root: str
    """
    ans2label = {}
    label2ans = []
    label = 0
    for answer in occurence:
        label2ans.append(answer)
        ans2label[answer] = label
        label += 1

    print('ans2lab', len(ans2label))
    print('lab2abs', len(label2ans))
    utils.create_dir(cache_root)

    cache_file = os.path.join(cache_root, name+'_ans2label.pkl')
    cPickle.dump(ans2label, open(cache_file, 'wb'))
    cache_file = os.path.join(cache_root, name+'_label2ans.pkl')
    cPickle.dump(label2ans, open(cache_file, 'wb'))
    return ans2label


def compute_target(answers_dset, ans2label, name, cache_root='data_RAD/cache'):
    """Augment answers_dset with soft score as label

    ***answers_dset should be preprocessed***

    Write result into a cache file
    """
    target = []
    count = 0
    for ans_entry in answers_dset:
        answers = preprocess_answer(ans_entry['answer'])
        # answer_count = {}
        # for answer in answers:
        #     answer_ = answer['answer']
        #     answer_count[answer_] = answer_count.get(answer_, 0) + 1

        labels = []
        scores = []
        if answers in ans2label:
            scores.append(1.)
            labels.append(ans2label[answers])
        # for answer in answer_count:
        #     if answer not in ans2label:
        #         continue
        #     labels.append(ans2label[answer])
        #     score = get_score(answer_count[answer])
        #     scores.append(score)

        target.append({
            'qid': ans_entry['qid'],
            'image_name': ans_entry['image_name'],
            'labels': labels,
            'scores': scores
        })

    utils.create_dir(cache_root)
    cache_file = os.path.join(cache_root, name+'_target.pkl')
    cPickle.dump(target, open(cache_file, 'wb'))
    return target


def get_answer(qid, answers):
    for ans in answers:
        if ans['question_id'] == qid:
            return ans


def get_question(qid, questions):
    for question in questions:
        if question['question_id'] == qid:
            return question


if __name__ == '__main__':
    VQA_dir = 'data_RAD'
    train_answers = json.load(open(VQA_dir + '/trainset.json'))

    answers = train_answers
    occurence = filter_answers(answers, 0)

    print('occ', (len(occurence)))
    cache_path = VQA_dir + '/cache/trainval_ans2label.pkl'
    if os.path.isfile(cache_path):
        print('found %s' % cache_path)
        ans2label = cPickle.load(open(cache_path, 'rb'))
    else:
        ans2label = create_ans2label(occurence, 'trainval')

    compute_target(train_answers, ans2label, 'train')

    test_answers = json.load(open(VQA_dir + '/testset.json'))
    compute_target(test_answers, ans2label, 'test')
