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#
# Pyserini: Reproducible IR research with sparse and dense representations
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json
import argparse
import string
from nltk import bigrams, word_tokenize, SnowballStemmer
from nltk.corpus import stopwords
from tqdm import tqdm
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Convert KILT Knowledge Source into a Document-level JSONL that can be processed by Pyserini')
parser.add_argument('--input', required=True, help='Path to the kilt_knowledgesource.json file')
parser.add_argument('--output', required=True, help='Path to the output directory and file name')
parser.add_argument('--bigrams', action='store_true', help='Enable bigrams')
parser.add_argument('--stem', action='store_true', help='Enable stemming on bigrams')
parser.add_argument('--flen', default=5903530, type=int, help='Number of lines in the file')
args = parser.parse_args()
FILE_LENGTH = args.flen
STOPWORDS = set(stopwords.words('english') + list(string.punctuation))
stemmer = SnowballStemmer("english")
with open(args.input, 'r') as f, open(f'{args.output}', 'w') as outp:
for line in tqdm(f, total=FILE_LENGTH, mininterval=10.0, maxinterval=20.0):
raw = json.loads(line)
doc = {}
doc["id"] = raw["_id"]
doc["contents"] = "".join(raw["text"])
if args.bigrams:
tokens = filter(lambda word: word.lower() not in STOPWORDS, word_tokenize(doc["contents"]))
if args.stem:
tokens = map(stemmer.stem, tokens)
bigram_doc = bigrams(tokens)
bigram_doc = " ".join(["".join(bigram) for bigram in bigram_doc])
doc["contents"] += " " + bigram_doc
doc["wikipedia_id"] = raw["wikipedia_id"]
doc["wikipedia_title"] = raw["wikipedia_title"]
doc["categories"] = raw["categories"]
_ = outp.write(json.dumps(doc))
_ = outp.write('\n')
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