import pandas as pd
import os
import gzip
import random
import re
from tqdm import tqdm
def get_all_files_in_directory(directory):
    all_files = []
    for root, dirs, files in os.walk(directory):
        root = root[len(directory):]
        if root.startswith('\\') or root.startswith('/'):
            root = root[1:]
        for file in files:
            file_path = os.path.join(root, file)
            all_files.append(file_path)
    return all_files

class Fileset(list):
    def __init__(self, path, ext='', _read=None):
        if isinstance(path, str):
            self.root = path
            self.extend(f for f in get_all_files_in_directory(self.root) if f.endswith(ext))
            self._read = _read

    def __getitem__(self, index):
        if isinstance(index, int):  # index是索引
            if self._read:
                return self._read(os.path.join(self.root, super().__getitem__(index)))
            else:
                return os.path.join(self.root, super().__getitem__(index))
        else:  # index是切片
            fileset = Fileset(None)
            fileset.root = self.root
            fileset._read = self._read
            fileset.extend(super().__getitem__(index))
            return fileset

def readOne(filePath):
    with gzip.open(filePath, 'rt', encoding='utf-8') if filePath.endswith('.gz') else open(filePath, encoding='utf-8') as f:
        retn = [line.strip() for line in f]
    return retn

rawcorpus = Fileset(r'D:\datasets\h-corpus\h-ss-corpus','.txt.gz', _read=readOne)
corpus = []
queries = []
qrels = []

reg_4 = re.compile(r'(.)\1{3,}')  # 匹配四个或更多连续相同的字符
def has_four_or_more_repeated_chars(text):
    return bool(reg_4.search(text))

def randsqidx(tmp):
    for i in range(20):  # 尝试20次
        sqidx = random.randint(10, len(tmp) - 10)
        if any(len(tmp[i]) < 20 or len(tmp[i]) > 512 or has_four_or_more_repeated_chars(tmp[i]) for i in range(sqidx-2, sqidx+3)):
            continue
        return sqidx
    return -1

def appendqrels(tmp, sqidx, _range, sr):
    qidx = len(queries)
    queries.append((qidx, tmp[sqidx]))
    if corpus:
        cidx = corpus[-1][0] + 3
    else:
        cidx = 2
    for k in _range:
        corpus.append((cidx+k, tmp[sqidx+k]))
        qrels.append((qidx, cidx+k, sr[k+2]))

def split3(s):
    retn = []
    cache = ''
    for one in s:
        cache += one
        if len(cache) < 64:
            continue
        if one in ('?', '!', '。', '？', '！'):
            retn.append(cache)
            cache = ''
    # print(retn)
    return retn

def main():
    for i in tqdm(range(len(rawcorpus)), desc="Converting"):
        tmp = rawcorpus[i]
        if len(tmp) < 30:
            continue
        if random.randint(0, 3):
            sqidx = randsqidx(tmp)
            if sqidx > 2:
                appendqrels(tmp, sqidx, (-2, -1, 1, 2), (0.95, 0.97, 1, 0.97, 0.95))
                continue
        for s in tmp:
            if len(s) <= 512:
                continue
            s = split3(s)
            if len(s) < 3:
                continue
            sqidx = random.randint(1, len(s)-2)
            appendqrels(s, sqidx, (-1, 1), (0.95, 1, 1, 1, 0.95))
            break

main()

corpus_pd = pd.DataFrame(corpus, columns=['cid', 'text'], dtype=str)
queries_pd = pd.DataFrame(queries, columns=['qid', 'text'], dtype=str)
qrels_pd = pd.DataFrame(qrels, columns=['qid', 'cid', 'score'], dtype=str)

# def load_dataset(path):
#     df = pd.read_parquet(path, engine="pyarrow")
#     return df
# corpus_pd = load_dataset(r"D:\datasets\H2Retrieval\data\corpus.parquet.gz")
# queries_pd = load_dataset(r"D:\datasets\H2Retrieval\data\queries.parquet.gz")
# qrels_pd = load_dataset(r"D:\datasets\H2Retrieval\data\qrels.parquet.gz")

corpus_pd['cid'] = corpus_pd['cid'].astype(str)
queries_pd['qid'] = queries_pd['qid'].astype(str)
qrels_pd['qid'] = qrels_pd['qid'].astype(str)
qrels_pd['cid'] = qrels_pd['cid'].astype(str)
qrels_pd['score'] = (qrels_pd['score']*100).astype(int)

corpus_pd.to_parquet(
    r"D:\datasets\H2Retrieval\data\corpus.parquet.gz",
    engine="pyarrow",
    compression="gzip",
)
queries_pd.to_parquet(
    r"D:\datasets\H2Retrieval\data\queries.parquet.gz",
    engine="pyarrow",
    compression="gzip",
)
qrels_pd.to_parquet(
    r"D:\datasets\H2Retrieval\data\qrels.parquet.gz",
    engine="pyarrow",
    compression="gzip",
)