import os
import pandas as pd
import rapidfuzz
import sys

from rapidfuzz.fuzz import partial_ratio
from rapidfuzz.utils import default_process


def read_arguments():
    try:
        path_glossary, path_in = sys.argv
        return path_glossary, path_in
    except Exception:
        print("ERROR: Wrong argument.")
        sys.exit(1)


def full_strip(line):
    return ' '.join(line.split())



def get_injected(sentence, sentence_en, sequence, inject):
    sen = sentence.split()
    sen_en = sentence_en.split()
    window_size = len(sequence.split())
    maxx = 0
    maxx_prv = 0
    maxxi = 0
    for i in range(len(sen) - window_size + 1):
        current = rapidfuzz.fuzz.ratio(' '.join(sen[i:i + window_size]), sequence)
        if current >= maxx:
            maxx_prv = maxx
            maxx = current
            maxxi = i
    if maxx_prv != maxx:
        return ' '.join(sen_en[:maxxi + window_size]) + ' $' + inject + '$ ' + ' '.join(sen_en[maxxi + window_size:])
    return sentence_en


THRESHOLD = 70

glossary_arg_path, in_arg_path = read_arguments()
train_in_path = os.path.join(os.path.expanduser('~'), in_arg_path)

glossary = pd.read_csv(os.path.join(os.path.expanduser('~'), glossary_arg_path), sep='\t')
glossary['source_lem'] = [str(default_process(x)) for x in glossary['source_lem']]
glossary['hash'] = [hash(x) for x in glossary['source']]
glossary = glossary[glossary['hash'] % 100 > 16]

file_en = pd.read_csv(train_in_path, sep='\t', header=None, names=['text'])
file_en['text'] = [default_process(text) for text in file_en['text'].values.tolist()]
file_en = file_en['text'].values.tolist()

file_en_lemmatized = pd.read_csv(train_in_path + '.lemmatized', sep='\t', header=None, names=['text'])
file_en_lemmatized['text'] = [default_process(text) for text in file_en_lemmatized['text'].values.tolist()]
file_en_lemmatized = file_en_lemmatized['text'].values.tolist()

en = []
translation_line_counts = []
for line, line_en in zip(file_en_lemmatized, file_en):
    line = default_process(line)
    matchez = rapidfuzz.process.extract(
        query=line, choices=glossary['source_lem'], limit=5, score_cutoff=THRESHOLD, scorer=partial_ratio)
    if len(matchez) > 0:
        for match in matchez:
            polish_translation = \
                glossary.loc[lambda df: df['source_lem'] == match[0]]['result'].astype(str).values.flatten()[0]
            en.append(get_injected(line, line_en, match[0], polish_translation))


with open(train_in_path, 'w') as file_en_write:
    for e in en:
        file_en_write.write(e + '\n')