import spacy
import copy
import pandas as pd
import rapidfuzz
from rapidfuzz.fuzz import partial_ratio
import time
from rapidfuzz.utils import default_process
import sys

spacy.require_gpu()

spacy_nlp_en = spacy.load('en_core_web_sm')
spacy_nlp_pl = spacy.load("pl_core_news_sm")


def read_arguments():
    try:
        corpus_path, glossary_path = sys.argv
        return corpus_path, glossary_path
    except:
        print("ERROR: Wrong argument amount.")
        sys.exit(1)



glossary = pd.read_csv('mt-summit-corpora/glossary.tsv', sep='\t', header=None, names=['source', 'result'])

source_lemmatized = []
for word in glossary['source']:
    temp = []
    for token in spacy_nlp_en(word):
        temp.append(token.lemma_)
    source_lemmatized.append(' '.join(temp).replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))

result_lemmatized = []
for word in glossary['result']:
    temp = []
    for token in spacy_nlp_pl(word):
        temp.append(token.lemma_)
    result_lemmatized.append(' '.join(temp).replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))

glossary['source_lem'] = source_lemmatized
glossary['result_lem'] = result_lemmatized
glossary = glossary[['source', 'source_lem', 'result', 'result_lem']]
glossary.to_csv('kompendium_lem.tsv', sep='\t')

corpus_path = 'mt-summit-corpora/train/'

skip_chars = ''',./!?'''

with open(corpus_path + 'in.tsv', 'r') as file:
    file_lemmatized = []
    for line in file:
        if len(file_lemmatized) % 10000 == 0:
            print(len(file_lemmatized), end='\r')
        temp = []
        for token in spacy_nlp_en(line):
            temp.append(token.lemma_)
        file_lemmatized.append(' '.join([x for x in temp if x not in skip_chars]).replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))

with open(corpus_path + 'expected.tsv', 'r') as file:
    file_pl_lemmatized = []
    for line in file:
        if len(file_pl_lemmatized) % 10000 == 0:
            print(len(file_lemmatized), end='\r')
        temp = []
        for token in spacy_nlp_pl(line):
            temp.append(token.lemma_)
        file_pl_lemmatized.append(' '.join([x for x in temp if x not in skip_chars]).replace(' - ', '-').replace(' ’', '’').replace(' / ', '/').replace(' ( ', '(').replace(' ) ', ')'))

THRESHOLD = 88

def is_injectable(sentence_pl, sequence):
    sen = sentence_pl.split()
    window_size = len(sequence.split())
    maxx = 0
    for i in range(len(sen) - window_size):
        current = rapidfuzz.fuzz.partial_ratio(' '.join(sen[i:i + window_size]), sequence)
        if current > maxx:
            maxx = current
    return maxx

def inject(sentence, sequence):
    sen = sentence.split()
    window_size = len(sequence.split())
    maxx = 0
    maxxi = 0
    for i in range(len(sen) - window_size):
        current = rapidfuzz.fuzz.partial_ratio(' '.join(sen[i:i + window_size]), sequence)
        if current > maxx:
            maxx = current
            maxxi = i
    return ' '.join(sen[:maxxi + window_size]) + ' ' \
           + glossary.loc[lambda df: df['source_lem'] == sequence]['result'].astype(str).values.flatten() \
           + ' ' + ' '.join(sen[maxxi + window_size:])

glossary = pd.read_csv('../kompendium_lem_cleaned.tsv', sep='\t', header=0, index_col=0)
glossary['source_lem'] = [default_process(x) for x in glossary['source_lem']]

start_time = time.time_ns()
en = []
translation_line_counts = []
for line, line_pl in zip(file_lemmatized, file_pl_lemmatized):
    if len(translation_line_counts) % 50000 == 0:
        print(str(len(translation_line_counts)) + '/' + str(len(file_lemmatized), end='\r'))
    line = default_process(line)
    line_pl = default_process(line_pl)
    matchez = rapidfuzz.process.extract(query=line, choices=glossary['source_lem'], limit=5, score_cutoff=THRESHOLD, scorer=partial_ratio)
    translation_line_counts.append(len(matchez))
    for match in matchez:
        # if is_injectable(line_pl, match[0]):
        en.append(inject(line, match[0])[0])


stop = time.time_ns()
timex = (stop - start_time) / 1000000000
print(timex)

tlcs = copy.deepcopy(translation_line_counts)

translations = pd.read_csv(corpus_path + 'expected.tsv', sep='\t', header=None, names=['text'])
with open(corpus_path + 'extected.tsv.injected.crossvalidated.pl', 'w') as file_pl:
    for line, translation_line_ct in zip(translations, tlcs):
        for i in range(translation_line_ct):
            file_pl.write(line)


with open(corpus_path + 'in.tsv.injected.crossvalidated.en', 'w') as file_en:
    for e in en:
        file_en.write(e + '\n')