from re import T
import pandas as pd
import csv
from collections import Counter, defaultdict
from nltk.tokenize import RegexpTokenizer
from nltk import trigrams
import regex as re
import lzma


class GapEssa:

    def __init__(self):
        self.alpha = 0.0001
        self.vocab = set()
        self.model = defaultdict(lambda: defaultdict(lambda: 0))       
        self.tokenizer = RegexpTokenizer(r"\w+")      

    def read_file(self, f, mode=0):
        for line in f:  
            text = line.split("\t") 
            if(mode==0):
                yield re.sub(r"[^\w\d'\s]+", '', re.sub(' +', ' ', ' '.join([text[6], text[7]]).replace("\\n"," ").replace("\n","").lower()))
            else:
                yield re.sub(r"[^\w\d'\s]+", '', re.sub(' +', ' ', text[7].replace("\\n"," ").replace("\n","").lower()))
        
    def train(self, f):
        with lzma.open(f, mode='rt') as file:
            for index, text in enumerate(self.read_file(file)):
                tokens = self.tokenizer.tokenize(text)
                for w1, w2, w3 in trigrams(tokens, pad_right=True, pad_left=True):
                    if w1 and w2 and w3:
                        self.model[(w2, w3)][w1] += 1
                        self.vocab.add(w1)
                        self.vocab.add(w2)
                        self.vocab.add(w3)
                if index == 40000:
                    break
            
            for pair in self.model:
                num_n_grams = float(sum(self.model[pair].values()))
                for word in self.model[pair]:
                    self.model[pair][word] = (self.model[pair][word] + self.alpha) / (num_n_grams + self.alpha*len(self.vocab))

    def out(self, input_f, output_f):        
        with open(output_f, 'w') as out_f:
            with lzma.open(input_f, mode='rt') as in_f:
                for _, text in enumerate(self.read_file(in_f, mode=1)):
                    t = self.tokenizer.tokenize(text)
                    if len(t) < 4:
                        # p = 'the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1'
                        p = 'the:0.03 be:0.03 to:0.03 of:0.025 and:0.025 a:0.025 in:0.020 that:0.020 have:0.015 I:0.010 it:0.010 for:0.010 not:0.010 on:0.010 with:0.010 he:0.010 as:0.010 you:0.010 do:0.010 at:0.010 :0.77'
                    else:
                        p = self.pred(t[0], t[1])
                    out_f.write(p + '\n')

    def pred(self, w1, w2):
        total = 0.0
        line = ''
        
        p = dict(self.model[w1, w2])
        m = dict(Counter(p).most_common(6))
        
        for word, prob in m.items():
            total += prob
            line += f'{word}:{prob} '

        if total == 0.0:
            return 'the:0.03 be:0.03 to:0.03 of:0.025 and:0.025 a:0.025 in:0.020 that:0.020 have:0.015 I:0.010 it:0.010 for:0.010 not:0.010 on:0.010 with:0.010 he:0.010 as:0.010 you:0.010 do:0.010 at:0.010 :0.77'
        if 1 - total >= 0.01:
            line += f":{1-total}"
        else:
            line += f":0.01"
        
        return line

wp = GapEssa()
wp.train('train/in.tsv.xz')
wp.out('dev-0/in.tsv.xz', 'dev-0/out.tsv')
wp.out('test-A/in.tsv.xz', 'test-A/out.tsv')