import pandas as pd
import numpy as np
import csv
import re
from collections import Counter, defaultdict
import nltk
import math
from tqdm import tqdm

directory = "train/in.tsv.xz"
directory_expected = "train/expected.tsv"
directory_dev_0 = "dev-0/in.tsv.xz"
directory_test_A = "test-A/in.tsv.xz"

class Model():
    
    def __init__(self, vocab_size=30_000, UNK_token= '<UNK>', n=3):
        if (n <= 1 or n % 2 == 0):
            raise "change N value !!!"
        self.n = n
        self.vocab_size = vocab_size
        self.UNK_token = UNK_token
    
    def train(self, corpus:list) -> None:
        if(self.n > 1):
            self.n_grams = list(nltk.ngrams(corpus, n=self.n))
        else:
            self.n_grams = corpus
        self.counter = Counter(self.n_grams)
        self.words_counter = Counter(corpus)
        self.all_quantities = Counter([gram[:math.floor(self.n/2)]+gram[math.ceil(self.n/2):] for gram in self.n_grams])

        self.all_grams = defaultdict(set)

        for gram in tqdm(self.n_grams):
            previous_words = tuple(gram[:math.floor(self.n/2)])
            next_words = tuple(gram[math.ceil(self.n/2):])
            word = gram[math.floor(self.n/2)]
            self.all_grams[(previous_words, next_words)].add(word)

    def get_conditional_prob_for_word(self, left_text: list, right_text: list, word: str) -> float:
        previous_words = tuple(left_text[-math.floor(self.n/2):])
        next_words = tuple(right_text[:math.floor(self.n/2)])
        quantity = self.counter[previous_words + tuple([word]) + next_words]
        all_quantity = self.all_quantities[previous_words + next_words]
        if (all_quantity <= 0):
            return 0
        return quantity/all_quantity
    
    def get_prob_for_text(self, text: list) -> float:
        prob = 1
        for gram in list(nltk.ngrams(text, self.n)):
            prob *= self.get_conditional_prob_for_word(gram[:math.floor(self.n/2)], gram[math.ceil(self.n/2):], gram[math.floor(self.n/2)])
        return prob
    
    def most_probable_words(self, left_text: list, right_text: list) -> str:
        previous_words = tuple(left_text[-math.floor(self.n/2):])
        next_words = tuple(right_text[:math.floor(self.n/2)])
        all_words = self.all_grams[(previous_words, next_words)]
        best_words = []
        for word in all_words:
            probability = self.get_conditional_prob_for_word(list(previous_words), list(next_words), word)
            best_words.append((word, probability))
        return sorted(best_words, key=(lambda l: l[1]), reverse=True)[:20]
    
    def generate_text(self, text_beggining:list, text_ending:list, greedy: bool) -> list:
        words = self.most_probable_words(text_beggining, text_ending)
        return words

dataframeList = pd.read_csv(directory, sep='\t', header=None, names=['FileId', 'Year', 'LeftContext', 'RightContext'], quoting=csv.QUOTE_NONE, chunksize=10000)

expectedList = pd.read_csv(directory_expected, sep='\t', header=None, names=['Word'], quoting=csv.QUOTE_NONE, chunksize=10000)

DATASET = ""

for number, (dataframe, expected) in enumerate(zip(dataframeList, expectedList)):
    dataframe = dataframe.reset_index()
    dataframe = dataframe.replace(r'\\r|\\n|\n|\\t', ' ', regex=True)

    expected['Word'] = expected['Word'].apply(lambda x: str(x).strip())
    word = expected['Word']

    left_text = dataframe['LeftContext'].to_list()
    right_text = dataframe['RightContext'].to_list()
    word = expected['Word'].to_list()

    lines = zip(left_text, word, right_text)
    lines = list(map(lambda l: " ".join(l), lines))
    DATASET = DATASET + " ".join(lines)

FINAL_DATASET = re.split(r"\s+", DATASET)
print(FINAL_DATASET[:100])

model_3gram = Model(n = 3)
model_3gram.train(FINAL_DATASET)

model = model_3gram

def convert_predictions(line):
    sum_predictions = np.sum([pred[1] for pred in line])
    result = ""
    all_pred = 0
    for word, pred in line:
        new_pred = math.floor(pred / sum_predictions * 100) / 100
        if(new_pred == 1.0):
            new_pred = 0.99
        all_pred = all_pred + new_pred
        result = result + word + ":" + str(new_pred) + " "
    if(round(all_pred, 2) < 1):
        result = result + ":" + str(round(1 - all_pred, 2))
    else:
        result = result + ":" + str(0.01)
    return result

# PREDICTION FOR DEV-0

dataframe = pd.read_csv(directory_dev_0, sep='\t', header=None, names=['FileId', 'Year', 'LeftContext', 'RightContext'], quoting=csv.QUOTE_NONE)
dataframe = dataframe.replace(r'\\r|\\n|\n|\\t', ' ', regex=True)

left_text = dataframe['LeftContext'].apply(lambda l: re.split(r"\s+", l)).to_list()
right_text = dataframe['RightContext'].apply(lambda l: re.split(r"\s+", l)).to_list()

lines = zip(left_text, right_text)
lines = list(map(lambda l: model.generate_text(l[0], l[1], False), tqdm(lines)))
print(lines[:100])

with open("dev-0/out.tsv", "w", encoding="UTF-8") as file:
    result = "\n".join(list(map(lambda l: convert_predictions(l), tqdm(lines))))
    file.write(result)
    file.close()

# PREDICTION FOR TEST-A

dataframe = pd.read_csv(directory_test_A, sep='\t', header=None, names=['FileId', 'Year', 'LeftContext', 'RightContext'], quoting=csv.QUOTE_NONE)
dataframe = dataframe.replace(r'\\r|\\n|\n|\\t', ' ', regex=True)

left_text = dataframe['LeftContext'].apply(lambda l: re.split(r"\s+", l)).to_list()
right_text = dataframe['RightContext'].apply(lambda l: re.split(r"\s+", l)).to_list()

lines = zip(left_text, right_text)
lines = list(map(lambda l: model.generate_text(l[0], l[1], False), tqdm(lines)))
print(lines[:100])

with open("test-A/out.tsv", "w", encoding="UTF-8") as file:
    result = "\n".join(list(map(lambda l: convert_predictions(l), tqdm(lines))))
    file.write(result)
    file.close()