evidence_corpus.py

import argparse
import pickle
import re
from collections import Counter
from os import walk, mkdir, makedirs
from os.path import relpath, join, exists
from typing import Set

from tqdm import tqdm

import config
from config import CORPUS_DIR, CORPUS_NAME_TO_PATH
from docqa.data_processing.text_utils import NltkAndPunctTokenizer
from docqa.triviaqa.read_data import normalize_wiki_filename
from docqa.utils import group, split, flatten_iterable
from bert.tokenization import BasicTokenizer


"""
Build and cache a tokenized version of the evidence corpus
Modified from docqa/triviaqa/evidence_corpus.py
"""

class ChineseTokenizer():
    def __init__(self):
        self._tokenizer = BasicTokenizer(do_lower_case=False)

    def tokenize_paragraph(self, paragraph):
        sentences = re.split("。|？|！", paragraph)
        ret = []
        for sent in sentences:
            if sent:
                ret.append(self._tokenizer.tokenize(sent) + ["。"])
        return ret

    def tokenize_paragraph_flat(self, paragraph):
        return self._tokenizer.tokenize(paragraph)


def _gather_files(input_root, output_dir, skip_dirs, wiki_only):
    if not exists(output_dir):
        mkdir(output_dir)

    all_files = []
    for root, dirs, filenames in walk(input_root):
        if skip_dirs:
            output = join(output_dir, relpath(root, input_root))
            if exists(output):
                continue
        path = relpath(root, input_root)
        normalized_path = normalize_wiki_filename(path)
        if not exists(join(output_dir, normalized_path)):
            mkdir(join(output_dir, normalized_path))
        all_files += [join(path, x) for x in filenames]
    if wiki_only:
        all_files = [x for x in all_files if "wikipedia/" in x]
    return all_files


def build_tokenized_files(filenames, input_root, output_root, tokenizer, override=True) -> Set[str]:
    """
    For each file in `filenames` loads the text, tokenizes it with `tokenizer, and
    saves the output to the same relative location in `output_root`.
    @:return a set of all the individual words seen
    """
    voc = set()
    for filename in filenames:
        out_file = normalize_wiki_filename(filename[:filename.rfind(".")]) + ".txt"
        out_file = join(output_root, out_file)
        if not override and exists(out_file):
            continue
        with open(join(input_root, filename), "r") as in_file:
            text = in_file.read().strip()
        paras = [x for x in text.split("\n") if len(x) > 0]
        paragraphs = [tokenizer.tokenize_paragraph(x) for x in paras]

        for para in paragraphs:
            for i, sent in enumerate(para):
                voc.update(sent)

        with open(join(output_root, out_file), "w") as in_file:
            in_file.write("\n\n".join("\n".join(" ".join(sent) for sent in para) for para in paragraphs))
    return voc


def build_tokenized_corpus(input_root, tokenizer, output_dir, skip_dirs=False,
                           n_processes=1, wiki_only=False):
    if not exists(output_dir):
        makedirs(output_dir)

    all_files = _gather_files(input_root, output_dir, skip_dirs, wiki_only)

    if n_processes == 1:
        voc = build_tokenized_files(tqdm(all_files, ncols=80), input_root, output_dir, tokenizer)
    else:
        voc = set()
        from multiprocessing import Pool
        with Pool(n_processes) as pool:
            chunks = split(all_files, n_processes)
            chunks = flatten_iterable(group(c, 500) for c in chunks)
            pbar = tqdm(total=len(chunks), ncols=80)
            for v in pool.imap_unordered(_build_tokenized_files_t,
                                         [[c, input_root, output_dir, tokenizer] for c in chunks]):
                voc.update(v)
                pbar.update(1)
            pbar.close()

    voc_file = join(output_dir, "vocab.txt")
    with open(voc_file, "w") as f:
        for word in sorted(voc):
            f.write(word)
            f.write("\n")


def _build_tokenized_files_t(arg):
    return build_tokenized_files(*arg)


def extract_voc(corpus, doc_ids):
    voc = Counter()
    for i, doc in enumerate(doc_ids):
        voc.update(corpus.get_document(doc, flat=True))
    return voc


def _extract_voc_tuple(x):
    return extract_voc(*x)


def get_evidence_voc(corpus, n_processes=1):
    doc_ids = corpus.list_documents()
    voc = Counter()

    if n_processes == 1:
        for doc in tqdm(doc_ids):
            voc = corpus.get_document(doc, flat=True)
    else:
        from multiprocessing import Pool
        chunks = split(doc_ids, n_processes)
        chunks = flatten_iterable(group(x, 10000) for x in chunks)
        pbar = tqdm(total=len(chunks), ncols=80)
        with Pool(n_processes) as pool:
            for v in pool.imap_unordered(_extract_voc_tuple, [[corpus, c] for c in chunks]):
                voc += v
                pbar.update(1)
        pbar.close()

    return voc


def build_evidence_voc(corpus, override, n_processes):
    target_file = join(corpus.directory, "vocab.txt")
    if exists(target_file) and not override:
        raise ValueError()
    voc = get_evidence_voc(XQAEvidenceCorpusTxt(), n_processes=n_processes).keys()
    with open(target_file, "w") as f:
        for word in sorted(voc):
            f.write(word)
            f.write("\n")


class XQAEvidenceCorpusTxt(object):
    """
    Corpus of the tokenized text from the given XQA evidence documents.
    Allows the text to be retrieved by document id
    """

    _split_all = re.compile("[\n ]")
    _split_para = re.compile("\n\n+")  # FIXME we should not have saved document w/extra spaces...

    def __init__(self, corpus_name, file_id_map=None):
        self.directory = join(CORPUS_DIR, corpus_name, "evidence")
        print("corpus name !: ", corpus_name)
        print("corpus path !: ", self.directory)

        self.file_id_map = file_id_map

    def get_vocab(self):
        with open(join(self.directory, "vocab.txt"), "r") as f:
            return {x.strip() for x in f}

    def load_word_vectors(self, vec_name):
        filename = join(self.directory, vec_name + "_pruned.pkl")
        if exists(filename):
            with open(filename, "rb"):
                return pickle.load(filename)
        else:
            return None

    def list_documents(self):
        if self.file_id_map is not None:
            return list(self.file_id_map.keys())
        f = []
        for dirpath, dirnames, filenames in walk(self.directory):
            if dirpath == self.directory:
                # Exclude files in the top level dir, like the vocab file
                continue
            if self.directory != dirpath:
                rel_path = relpath(dirpath, self.directory)
                f += [join(rel_path, x[:-4]) for x in filenames]
            else:
                f += [x[:-4] for x in filenames]
        return f

    def get_document(self, doc_id, n_tokens=None, flat=False):
        if self.file_id_map is None:
            file_id = doc_id
        else:
            file_id = self.file_id_map.get(doc_id)

        if file_id is None:
            return None

        file_id = join(self.directory, file_id + ".txt")
        if not exists(file_id):
            return None

        with open(file_id, "r") as f:
            if n_tokens is None:
                text = f.read()
                if flat:
                    return [x for x in self._split_all.split(text) if len(x) > 0]
                else:
                    paragraphs = []
                    for para in self._split_para.split(text):
                        paragraphs.append([sent.split(" ") for sent in para.split("\n")])
                    return paragraphs
            else:
                paragraphs = []
                paragraph = []
                cur_tokens = 0
                for line in f:
                    if line == "\n":
                        if not flat and len(paragraph) > 0:
                            paragraphs.append(paragraph)
                            paragraph = []
                    else:
                        sent = line.split(" ")
                        sent[-1] = sent[-1].rstrip()
                        if len(sent) + cur_tokens > n_tokens:
                            if n_tokens != cur_tokens:
                                paragraph.append(sent[:n_tokens-cur_tokens])
                            break
                        else:
                            paragraph.append(sent)
                            cur_tokens += len(sent)
                if flat:
                    return flatten_iterable(paragraph)
                else:
                    if len(paragraph) > 0:
                        paragraphs.append(paragraph)
                    return paragraphs


def main():
    parse = argparse.ArgumentParser("Pre-tokenize the XQA evidence corpus")
    parse.add_argument("--corpus",
                       choices=["en", "fr", "de", "ru", "pt", "zh", "pl", "uk", "ta",
                                "en_trans_de", "en_trans_zh",
                                "fr_trans_en", "de_trans_en", "ru_trans_en", "pt_trans_en",
                                "zh_trans_en", "pl_trans_en", "uk_trans_en", "ta_trans_en"],
                       required=True)
    # This is slow, using more processes is recommended
    parse.add_argument("-n", "--n_processes", type=int, default=1, help="Number of processes to use")
    parse.add_argument("--wiki_only", action="store_true")
    args = parse.parse_args()

    output_dir = join(config.CORPUS_DIR, args.corpus, "evidence")
    source = join(config.CORPUS_NAME_TO_PATH[args.corpus], "evidence")
    if args.corpus == "en_trans_zh" or args.corpus == "zh":
        tokenizer = ChineseTokenizer()
    else:
        tokenizer = NltkAndPunctTokenizer()
    build_tokenized_corpus(source, tokenizer, output_dir,
                           n_processes=args.n_processes, wiki_only=args.wiki_only)

if __name__ == "__main__":
    main()