"""
Preprocess the WikiNER dataset, by
1) normalizing tags;
2) split into train (70%), dev (15%), test (15%) datasets.
"""

import os
import random
import warnings
from collections import Counter

def read_sentences(filename, encoding):
    sents = []
    cache = []
    skipped = 0
    skip = False
    with open(filename, encoding=encoding) as infile:
        for i, line in enumerate(infile):
            line = line.rstrip()
            if len(line) == 0:
                if len(cache) > 0:
                    if not skip:
                        sents.append(cache)
                    else:
                        skipped += 1
                        skip = False
                    cache = []
                continue
            array = line.split()
            if len(array) != 2:
                skip = True
                warnings.warn("Format error at line {}: {}".format(i+1, line))
                continue
            w, t = array
            cache.append([w, t])
        if len(cache) > 0:
            if not skip:
                sents.append(cache)
            else:
                skipped += 1
            cache = []
    print("Skipped {} examples due to formatting issues.".format(skipped))
    return sents

def write_sentences_to_file(sents, filename):
    print(f"Writing {len(sents)} sentences to {filename}")
    with open(filename, 'w', encoding='utf-8') as outfile:
        for sent in sents:
            for pair in sent:
                print(f"{pair[0]}\t{pair[1]}", file=outfile)
            print("", file=outfile)

def remap_labels(sents, remap):
    new_sentences = []
    for sentence in sents:
        new_sent = []
        for word in sentence:
            new_sent.append([word[0], remap.get(word[1], word[1])])
        new_sentences.append(new_sent)
    return new_sentences

def split_wikiner_data(directory, sents, prefix="", suffix="bio", remap=None, shuffle=True, train_fraction=0.7, dev_fraction=0.15, test_section=True):
    random.seed(1234)

    if remap:
        sents = remap_labels(sents, remap)

    # split
    num = len(sents)
    train_num = int(num*train_fraction)
    if test_section:
        dev_num = int(num*dev_fraction)
        if train_fraction + dev_fraction > 1.0:
            raise ValueError("Train and dev fractions added up to more than 1: {} {} {}".format(train_fraction, dev_fraction))
    else:
        dev_num = num - train_num

    if shuffle:
        random.shuffle(sents)
    train_sents = sents[:train_num]
    dev_sents = sents[train_num:train_num+dev_num]
    if test_section:
        test_sents = sents[train_num+dev_num:]
        batches = [train_sents, dev_sents, test_sents]
        filenames = [f'train.{suffix}', f'dev.{suffix}', f'test.{suffix}']
    else:
        batches = [train_sents, dev_sents]
        filenames = [f'train.{suffix}', f'dev.{suffix}']

    if prefix:
        filenames = ['%s.%s' % (prefix, f) for f in filenames]
    for batch, filename in zip(batches, filenames):
        write_sentences_to_file(batch, os.path.join(directory, filename))

def split_wikiner(directory, *in_filenames, encoding="utf-8", **kwargs):
    sents = []
    for filename in in_filenames:
        new_sents = read_sentences(filename, encoding)
        print(f"{len(new_sents)} sentences read from {filename}.")
        sents.extend(new_sents)

    split_wikiner_data(directory, sents, **kwargs)

if __name__ == "__main__":
    in_filename = 'raw/wp2.txt'
    directory = "."
    split_wikiner(directory, in_filename)