from datasets import load_dataset
import random
import argparse
import csv
import glob
import pandas as pd
from sklearn.model_selection import train_test_split


def main(args):
    trans_chars = ",.:;!"
    
    filelist = glob.glob('../langid/sentences/*.txt')

    data = pd.DataFrame()

    for tsvfile in filelist:
        print(f"Processing {tsvfile}")
        tmp = pd.read_csv(tsvfile, sep='\t',on_bad_lines='skip',engine='python',encoding='utf8')
        if len(tmp.columns)==1:
            tmp.insert(0,'id','')
        
        tmp.columns=['id','source']   
        data=pd.concat([data,tmp])

   
    # Trim
    data['source'] = data['source'].str.strip()
    
    # Drop rows that does not end with punctation
    data = data[data['source'].str[-1:].isin([".",",",":",";","!"])]
    
    # For not creating chaos later with . . . Just remove examples with elipsis 
    data = data[~data['source'].str.contains("...", regex=False)] 
    data = data[~data['source'].str.contains(". . .", regex=False)]
   
    #Drop the id
    data = data.drop(['id'],axis=1)
    
    # Duplicate column
    data['target'] = data['source']
   
    # Apply each method to 10% of the corpus
    # set a stop
    stop =int(len(data)/10)
    stop_small = int(stop/2)

    #Main shuffling
    data = data.sample(frac=1).reset_index(drop=True)
    
    # Lowercase in 10% of the cases
    data.loc[:stop,'source'] = data['source'].str.lower()
    print(f"Lower casing 10% - '{data.loc[0]['target']}' -> '{data.loc[0]['source']}'")
    data = data.sample(frac=1).reset_index(drop=True)

    # Uppercase in 5% of the cases
    data.loc[:stop_small,'source'] = data['source'].str.upper()
    print(f"Upper casing 5% - '{data.loc[0]['target']}' -> '{data.loc[0]['source']}'")
    data = data.sample(frac=1).reset_index(drop=True)


    # Remove all spaces in 10% of the cases
    data.loc[:stop,'source'] = data['source'].str.replace(" ","")
    print(f"Removing space 10% - '{data.loc[0]['target']}' -> '{data.loc[0]['source']}'")
    data = data.sample(frac=1).reset_index(drop=True)

    # Remove both spaces and do lowercasing in 10%
    data.loc[:stop,'source'] = data['source'].str.replace(" ","")
    data.loc[:stop,'source'] = data['source'].str.lower()
    print(f"Removing space and doing lovercase 10% - '{data.loc[0]['target']}' -> '{data.loc[0]['source']}'")
    data = data.sample(frac=1).reset_index(drop=True)
    
    # Remove a random number of spaces in 10% of the cases
    for index, row in data[0:stop].iterrows():
        source = row['source']
        #Find the spaces
        spacepos = [pos for pos, char in enumerate(source) if char == " "]
        random.shuffle(spacepos)
        
        #Reduce to a random number
        spacepos = spacepos[0:random.randint(0,len(spacepos))]
        
        ##Sort in reverse order
        spacepos.sort(reverse=True)
        
        ##Loop and replace
        for s in spacepos:
            source = source[:s] + source[s+1:]

        data.loc[index,'source'] = source
    
    print(f"Removing a random number of spaces 10% - '{data.loc[0]['target']}' -> '{data.loc[0]['source']}'")
    data = data.sample(frac=1).reset_index(drop=True)

    # Remove all punctation in 10% of the cases
    trans_table = source.maketrans("", "", trans_chars)
    data.loc[:stop,'source'] = data['source'].str.translate(trans_table)
    print(f"Removing punctation 10% - '{data.loc[0]['target']}' -> '{data.loc[0]['source']}'")
    data = data.sample(frac=1).reset_index(drop=True)
    
    # Remove a random number of commas in 10% of the cases
    for index, row in data[0:stop].iterrows():
        source = row['source']
        #Find the spaces
        spacepos = [pos for pos, char in enumerate(source) if char == ", "]
        random.shuffle(spacepos)

        #Reduce to a random number
        spacepos = spacepos[0:random.randint(0,len(spacepos))]

        ##Sort in reverse order
        spacepos.sort(reverse=True)

        ##Loop and replace
        for s in spacepos:
            source = source[:s] + " " +source[s+1:]

        data.loc[index,'source'] = source

    print(f"Removing a random number of commas 10% - '{data.loc[0]['target']}' -> '{data.loc[0]['source']}'")
    data = data.sample(frac=1).reset_index(drop=True)
    data.loc[:,'source'] = "correct: "+data['source']
    

    # Train - test - dev
    train, test = train_test_split(data, test_size=0.2)
    test, dev = train_test_split(test, test_size=0.5)
    
    # Write the datasets to disk
    train.to_csv('correct_datafiles/correct_train.tsv', index=False, header=False, sep='\t')
    test.to_csv('correct_datafiles/correct_test.tsv', index=False, header=False, sep='\t')
    dev.to_csv('correct_datafiles/correct_dev.tsv', index=False, header=False, sep='\t')

def parse_args():
    # Parse commandline
    parser = argparse.ArgumentParser()
    args = parser.parse_args()
    return args

if __name__ == "__main__":
    args = parse_args()
    main(args)