import csv
from lib2to3.pytree import Base
from logging import raiseExceptions
import pandas as pd
import regex as re
import nltk
import sys

from nltk import trigrams, word_tokenize
from collections import Counter, defaultdict

# nltk.download("punkt")

# train set
train_data = pd.read_csv(
    "train/in.tsv.xz",
    sep="\t",
    error_bad_lines=False,
    warn_bad_lines=False,
    header=None,
    quoting=csv.QUOTE_NONE,
    nrows=100_000
)

# training labels
train_labels = pd.read_csv(
    "train/expected.tsv",
    sep="\t",
    error_bad_lines=False,
    warn_bad_lines=False,
    header=None,
    quoting=csv.QUOTE_NONE,
    nrows=100_000
)

# dev set
dev_data = pd.read_csv(
    "dev-0/in.tsv.xz",
    sep="\t",
    error_bad_lines=False,
    warn_bad_lines=False,
    header=None,
    quoting=csv.QUOTE_NONE,
)

# test set
test_data = pd.read_csv(
    "test-A/in.tsv.xz",
    sep="\t",
    error_bad_lines=False,
    warn_bad_lines=False,
    header=None,
    quoting=csv.QUOTE_NONE,
)

class Model():
    def __init__(self, vocab_size, alpha):
        self.alpha = alpha
        self.model = defaultdict(lambda: defaultdict(lambda: 0))
        self.vocab = set()
        self.vocab_size = vocab_size

    def train(self, corpus: list):
        for _, row in corpus[:self.vocab_size].iterrows():
            text = prepare_text(str(row["final"]))
            words = word_tokenize(text)
            for w1, w2, w3 in trigrams(words, pad_right=True, pad_left=True):
                if all([w1, w2, w3]):
                    self.vocab.add(w1)
                    self.vocab.add(w2)
                    self.vocab.add(w3)
                    self.model[(w2, w3)][w1] += 1
                    self.model[(w1, w2)][w3] += 1

        for w_pair in self.model:
            ngram_count = float(sum(self.model[w_pair].values()))
            denominator = ngram_count + self.alpha * len(self.vocab)
            for w3 in self.model[w_pair]:
                nominator = self.model[w_pair][w3] + self.alpha
                self.model[w_pair][w3] = nominator / denominator

    def predict(self, word1, word2):
        raw_prediction = dict(self.model[word1, word2])
        prediction = dict(Counter(raw_prediction).most_common(6))

        total_prob = 0.0
        str_prediction = ""

        for word, prob in prediction.items():
            total_prob += prob
            str_prediction += f"{word}:{prob} "

        remaining_prob = 1 - total_prob

        str_prediction += f":{remaining_prob}"

        return str_prediction


def prepare_text(text):
    text = text.lower().replace("-\\n", "").replace("\\n", " ")
    text = re.sub(r"\p{P}", "", text)

    return text

# def write_output():
#     with open('dev-0/out.tsv', 'w') as file:
#         for _, row in dev_data.iterrows():
#             left_text, right_text = prepare_text(str(row[6])), prepare_text(str(row[7]))
#             left_words, right_words = word_tokenize(left_text), word_tokenize(right_text)
#             if len(left_words) < 2 or len(right_words) < 2:
#                 prediction = ':1.0'
#             else:
#                 prediction = model.predict(left_words[len(left_words) - 1], right_words[0])
#             file.write(prediction + '\n')

#     with open('test-A/out.tsv', 'w') as file:
#         for _, row in test_data.iterrows():
#             left_text, right_text = prepare_text(str(row[6])), prepare_text(str(row[7]))
#             left_words, right_words = word_tokenize(left_text), word_tokenize(right_text)
#             if len(left_words) < 2 or len(right_words) < 2:
#                 prediction = ':1.0'
#             else:
#                 prediction = model.predict(left_words[len(left_words) - 1], right_words[0])
#             file.write(prediction + '\n')

def write_output():
    with open("dev-0/out.tsv", "w") as file:
        for _, row in dev_data.iterrows():
            text = prepare_text(str(row[7]))
            words = word_tokenize(text)
            if len(words) < 3:
                prediction = "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
            else:
                prediction = model.predict(words[0], words[1])
            file.write(prediction + "\n")

    with open("test-A/out.tsv", "w") as file:
        for _, row in test_data.iterrows():
            text = prepare_text(str(row[7]))
            words = word_tokenize(text)
            if len(words) < 3:
                prediction = "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
            else:
                prediction = model.predict(words[0], words[1])
            file.write(prediction + "\n")
                
if __name__ == "__main__":
    # Preapare train data
    print("Preparing data...")
    train_data = train_data[[6, 7]]
    train_data = pd.concat([train_data, train_labels], axis=1)
    train_data["final"] = train_data[6] + train_data[0] + train_data[7]
    
    # declare model
    print("Preparing model...")
    model = Model(100_000, 0.0001)

    # train model
    print("Model training...")
    model.train(train_data)

    # write outputs
    print("Writing outputs...")
    write_output()